Fuzzy swarm logic for plagiarism detection

Abstract

Plagiarism detection is a critical challenge in the digital age, complicated by paraphrasing, cross-language translation, and source code plagiarism that evade simple string matching. Conventional detection methods often struggle with such nuanced cases. Fuzzy swarm logic – which combines fuzzy logic’s capacity to handle uncertainty with swarm intelligence’s optimisation abilities – offers a promising approach for plagiarism detection, especially in complex scenarios like cross-language or paraphrased content.

This article presents an in-depth technical review of fuzzy swarm logic methods for plagiarism detection. We discuss how fuzzy logic-based systems evaluate similarity in a flexible, human-like manner by using degrees of similarity instead of rigid thresholds. We also examine swarm intelligence techniques, such as particle swarm optimisation, that optimise similarity assessment and feature selection processes. Key examples include a cross-language plagiarism detection system using fuzzy swarm-based summarisation to compare content across different languages. There are also fuzzy clustering frameworks that capture semantic similarity at multiple granularities. Additionally, hybrid models integrating fuzzy logic with modern language modelling techniques show how these approaches can assess plagiarism severity and detect paraphrased plagiarism more effectively than traditional methods. We compare these fuzzy and swarm-based techniques with conventional and deep learning approaches. We highlight their strengths (such as adaptability and interpretability) and identify areas for improvement. The review concludes that fuzzy swarm logic enhances the flexibility, accuracy, and contextual awareness of plagiarism detection systems. This makes it a valuable component in current and future anti-plagiarism technologies.

Fuzzy swarm logic provides a framework that is inherently adaptable, allowing systems to measure similarity in degrees and make informed judgments even when plagiarism is partial or disguised.

Introduction

Plagiarism is the act of using someone else’s work or ideas without proper attribution. It undermines academic integrity and creativity. It also poses a serious problem in educational, research, and creative communities. The proliferation of digital content and easy access to information have made plagiarism both easier to commit and harder to detect. Extrinsic plagiarism detection (comparing a suspect document against external sources) and intrinsic detection (analysing style changes within a document) are two broad strategies traditionally employed to catch plagiarism. However, plagiarism today often involves sophisticated obfuscation techniques such as paraphrasing text, translating content into other languages, or modifying source code structure. These tactics can fool straightforward detection algorithms.

Conventional plagiarism detection methods typically rely on exact or near-exact text matching. They use techniques like string matching, n-gram overlap, TF–IDF cosine similarity, or edit distance algorithms to identify copied passages. These methods work well for verbatim copy-paste plagiarism but they have significant limitations. For instance, a simple cosine similarity might treat two documents as identical in meaning even if one is a well-crafted paraphrase of the other, because standard cosine similarity ignores word order and context. Similarly, direct keyword matching fails when synonyms are used or when sentence structure is altered. Cross-language plagiarism (where the source and plagiarised text are in different languages) adds another layer of complexity – traditional detectors cannot match text across languages without translation. In the realm of computer programs, plagiarists may rename variables, reorder functions, or switch programming languages, rendering naive text-based comparisons ineffective.

To overcome these challenges, researchers have explored advanced artificial intelligence (AI) techniques for plagiarism detection. Two promising directions are fuzzy logic and swarm intelligence, which can also be combined into what we refer to as fuzzy swarm logic. Fuzzy logic introduces a way to model uncertainty and partial truth, aligning well with the notion of partial plagiarism or borderline similarity. Instead of outputting a binary judgment (plagiarised or not) based on a hard threshold, a fuzzy system can assign a degree of plagiarism or a similarity score that reflects how closely the content matches known sources. This mirrors human judgment more closely – people can recognise when an essay is somewhat similar to a source versus very closely copied, rather than just a yes/no decision.

On the other hand, swarm intelligence refers to optimisation algorithms inspired by the collective behaviour of social organisms (like flocks of birds or schools of fish). Algorithms such as Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) fall into this category. They are powerful at searching large solution spaces and optimising complex objective functions. In plagiarism detection, swarm algorithms can be used to optimise various aspects of the detection process. For example, a swarm algorithm might tune the weights of different features (e.g. exact matches, semantic similarity, structural similarity, stylistic differences) to maximise detection performance. Alternatively, it might be used to select an optimal subset of document fragments that capture the essence of a text without needing to compare every line. Swarm-based strategies could even align pieces of text or code intelligently by mimicking cooperative behaviour.

Fuzzy swarm logic merges these two paradigms: it leverages fuzzy logic’s nuanced decision-making with swarm intelligence’s global optimisation. The combination is particularly useful for complex plagiarism scenarios that involve multiple factors or require balancing trade-offs. For instance, in a cross-language scenario one must account for differences in languages, paraphrasing, and translation errors. A fuzzy system can evaluate similarity in an imprecise, tolerant way, and a swarm algorithm can optimise the matching of content across languages or the selection of representative features. By combining them, we can create detection systems that are both flexible and efficient.

This paper provides a detailed technical review of fuzzy swarm logic approaches in plagiarism detection. We first cover the background concepts of plagiarism detection and the basics of fuzzy logic and swarm intelligence in this context. Next, we delve into specific methods and systems that have been proposed or implemented, highlighting how they work and their performance. This includes systems that use fuzzy logic to improve plagiarism detection accuracy and user-friendliness [1]. We also discuss those that incorporate swarm intelligence for tasks like summarising content and finding cross-lingual similarities [2]. In each case, we examine how these approaches improve upon conventional methods. Finally, we consider other modern plagiarism detection approaches (such as deep learning-based methods) and discuss how fuzzy swarm logic compares to or can complement these techniques. By the end, we outline potential directions for future research, ensuring that plagiarism detectors remain effective against increasingly cunning forms of plagiarism.

Background

Plagiarism detection challenges and traditional approaches

Plagiarism detection has traditionally been framed as a problem of finding textual similarities between documents. In extrinsic plagiarism detection, a suspicious document is compared to a corpus of source documents (such as academic papers, websites, or previous student assignments) to find overlapping content. Early systems like content-matching software employed straightforward algorithms – scanning for identical phrases or computing similarity scores using common measures. One basic method is to break documents into word sequences (n-grams) and count how many n-grams two documents share. Another is to represent documents as high-dimensional vectors of word frequencies and use the cosine similarity between these vectors as a measure of likeness. If a similarity score exceeds a chosen threshold, the system flags plagiarism.

Such approaches, while computationally simple, are brittle in the face of clever obfuscation. Students and writers intent on plagiarism can easily evade detection by making superficial changes: replacing words with synonyms, changing a sentence from active voice to passive, or reordering sentences. These edits preserve the core content but can sharply reduce n-gram overlap and alter the word frequency profile, thus lowering cosine similarity below detection thresholds. In addition, fixed thresholds themselves are problematic – set them too high and you miss subtle plagiarism; set them too low and you get many false positives (innocent texts flagged due to common phrases).

Paraphrasing is a particularly challenging form of plagiarism. A well-paraphrased passage may share little vocabulary with the original yet still clearly derive from it in meaning. Traditional detection struggles here because it relies on surface-level similarity. More advanced natural language processing techniques have been introduced, such as semantic similarity measures that use thesauri or word embeddings to catch similarity in meaning rather than exact wording. These help, but quantifying semantic overlap in a reliable way remains difficult. For example, two sentences can be semantically similar in a general sense but have differences in nuance that are hard to measure automatically.

Cross-language plagiarism adds the complication of translation. Someone might take an English text and translate it into Spanish, claiming it as original work in Spanish. Without tools to bridge languages, a detection system limited to one language would not catch this. One solution is to translate everything into a common language and then compare, but machine translation can introduce errors or alter wording enough to confound simple matching. There have been methods that use bilingual dictionaries or cross-language semantic models to tackle this challenge. It remains an active research area because languages vary greatly in structure and vocabulary, and direct translation may not capture subtle plagiarised correspondences.

In intrinsic plagiarism detection, the goal is to spot changes in writing style within one document without reference to external sources. Methods analyse features like sentence-length distribution, vocabulary richness, or stylistic markers to detect sections that stand out from the rest of the document. While intrinsic methods are important, they are outside the scope of this paper’s focus on fuzzy and swarm-based techniques, which have mostly been applied in extrinsic detection scenarios.

Traditional plagiarism detectors also face challenges with source code plagiarism. Source code is text, but it has syntax and structure; plagiarised code may be reformatted or have variables renamed yet still implement the same logic. Dedicated code plagiarism tools often parse the code into abstract syntax trees or intermediate representations to detect structural similarity beyond textual differences. Here too, clever disguises like changing the programming language (e.g. rewriting a Java program in C++) can defeat simple approaches that are language-specific.

Given these challenges, the detection of plagiarism – especially in nuanced cases like paraphrasing or cross-language copying – cannot rely solely on rigid exact-matching algorithms. It requires intelligent systems that can understand similarity in a more flexible way and optimise the search for evidence of plagiarism across large and complex search spaces. This is where fuzzy logic and swarm intelligence come into play.

Fuzzy logic for flexible similarity evaluation

Fuzzy logic is a mathematical framework for dealing with uncertainty and partial truths. Unlike classical Boolean logic that deems statements strictly true or false, fuzzy logic allows statements to have degrees of truth represented by a value between 0 and 1. This makes it well-suited for reasoning in situations that are not black-and-white – which is often the case in plagiarism detection. For example, consider the statement: “Document A is similar to Document B.” Traditional systems might say this is either true or false based on a threshold (e.g. true if similarity > 0.8, false otherwise). Fuzzy logic, by contrast, would let us say “Document A is similar to Document B to a degree of 0.6,” reflecting moderate similarity.

In a fuzzy logic system, we define fuzzy sets and membership functions for the concepts of interest. For plagiarism detection, one could define fuzzy sets like “highly similar”, “moderately similar”, and “not similar” with corresponding membership functions that map a numerical similarity score (like a percentage of overlap) to a membership value in each set. A similarity of 80% might have high membership in “highly similar”, whereas 30% might have partial membership in “moderately similar”. These membership values can then be used in a set of fuzzy if–then rules. For instance:

• “IF the overlap is High OR the synonym-match is High THEN the plagiarism risk is High.”
• “IF the overlap is Medium AND the writing-style change is Low THEN the plagiarism risk is Medium.”

A fuzzy inference engine evaluates such rules on the input data (various similarity metrics, stylistic features, etc.) and produces an output that is a fuzzy value (like a plagiarism-risk score). The system then defuzzifies this output into a crisp value or category if needed – for example, a final percentage or a yes/no decision with a confidence level.

The advantage of using fuzzy logic in plagiarism detection is the ability to incorporate multiple criteria and to handle borderline cases gracefully. Rather than setting arbitrary cutoffs, the system can reason that, for example, a document with 50% identical sentences might still be considered plagiarised if those sentences occur in suspicious contexts or if other indicators are present. Conversely, a document with 10% identical text might be innocent if those overlaps are just common phrases or properly cited quotes. Fuzzy logic allows these interpretations because it accumulates evidence in a gradual way instead of making a snap binary judgment.

Researchers have found fuzzy logic to improve plagiarism detection effectiveness. In one study, a plagiarism detection system built on fuzzy logic achieved high accuracy and users found it to be user-friendly in interpreting results [1]. By evaluating the degree of similarity rather than using a single rigid cutoff, the system could flag potential plagiarism with nuanced scoring, making it easier for instructors or editors to make final judgments [1]. The fuzzy approach essentially replicates an expert’s intuition in software – considering various factors (exact matches, paraphrase similarity, writing style differences) and balancing them to gauge plagiarism likelihood.

Another domain of application is in semantic similarity measures. There have been plagiarism detection techniques that use fuzzy semantic-based string similarity [1]. This means they measure how similar two strings are in meaning using fuzzy logic. For example, “in my opinion, this is significant” vs “I think this is important” might be considered a close match semantically, even though they share few exact words. A fuzzy semantic similarity algorithm can assign a high similarity score to such a pair, whereas a naive exact-match algorithm would not. By using fuzzy sets for semantic relations (like synonyms, antonyms, or generalisation of terms), one can capture the idea that certain words are “sort of similar” to certain other words, with a graded score rather than a binary yes/no.

In summary, fuzzy logic contributes to plagiarism detection by making the system’s decision process more flexible and human-like. It acknowledges that plagiarism is not an absolute concept – there are degrees and contexts to consider – and provides a formal way to deal with this uncertainty. However, fuzzy logic on its own does not solve all problems; it needs to be complemented with robust ways of extracting or comparing features from texts. This is where other AI techniques, including swarm intelligence, come into the picture.

Swarm intelligence for optimisation in detection

Swarm intelligence refers to algorithms that take inspiration from the collective behaviour of decentralised, self-organising systems in nature. Common examples include ant colonies finding the shortest paths to food, bird flocks synchronising flight patterns, or fish schools weaving around predators. In computer science, these behaviours are abstracted into optimisation algorithms that can find good (often near-optimal) solutions to complex problems by iterative improvement and cooperation among simple agents.

Two popular swarm-based algorithms are Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO). PSO uses agents called “particles” which move through the search space of possible solutions. They adjust their positions based on their own experience and their neighbours’ experiences. ACO simulates ants laying pheromone trails to prefer certain paths in a graph, which is useful for combinatorial problems like finding good routes or matchings.

In the context of plagiarism detection, one might wonder what exactly needs optimising. There are several possibilities:

• Feature weighting/tuning: A plagiarism detector might compute multiple similarity features between documents (literal word overlap, semantic similarity, structural similarity, stylistic differences, etc.). Determining how to weight these features to produce the best overall detection accuracy is a complex optimisation problem. PSO can be employed to automatically find the optimal set of weights or thresholds that maximise performance on a training set of plagiarised and non-plagiarised document pairs.
• Selection of document fragments: Especially for long documents, it might be inefficient to compare every part of the text to every part of another text. A swarm algorithm could be used to pick the most informative subset of sentences or paragraphs from a document that should be compared to sources. Think of it as summarising a text in a way that captures the likely plagiarised content. This can be cast as an optimisation problem: selecting a subset of sentences that maximises coverage of the document’s main content while minimising redundancy.
• Matching and alignment: In cross-language or heavily paraphrased scenarios, direct matching of text segments is tricky. However, one could imagine a swarm-based approach where a set of possible alignments between segments of the suspect and source documents is represented by agents, and these agents evolve to maximise an overall similarity score. This is more speculative, but it aligns with how ACO has been used in some text alignment tasks – agents cooperatively finding the best correspondence between pieces of text.

There are concrete examples of swarm intelligence being applied to plagiarism detection problems. One example is using PSO for document summarisation in service of plagiarism detection [2]. Essentially, the system condenses a document into a shorter form capturing its key points, which can then be translated or compared to other summaries. The optimisation comes in deciding which sentences to include in the summary. PSO is used to search for the best combination of sentences that yields the most representative summary. This is guided by a fitness function that considers factors like coverage of main topics, coherence of the summary, and removal of redundant information.

Another example is using PSO to fine-tune similarity measures. Researchers have proposed a hybrid fine-tuned weighted harmonic mean model that incorporates Hamming, Cosine, and Jaccard similarity scores. The PSO algorithm adjusts the weight of each metric to highlight even small similarities that could indicate plagiarism. Essentially, the PSO “learns” the best weighting scheme to improve detection on validation data. This approach outperformed a simple unweighted average of metrics, showing how swarm optimisation can yield a more sensitive detector.

Swarm algorithms can also help address the challenge of efficiency. Checking a document against millions of potential sources (like the entire internet) is computationally expensive. Heuristic techniques borrowed from swarm intelligence might guide the search to likely sources faster. For instance, an ant-colony-like strategy could perform an intelligent crawl through reference materials: early finds of matching content would attract more search effort in those areas (analogous to stronger pheromone trails). This way, instead of blindly comparing the query document to every document in a repository, the system dynamically focuses on promising leads.

In summary, swarm intelligence provides powerful tools for optimising components of plagiarism detection systems. By mimicking natural problem-solving strategies, swarm algorithms can find effective solutions in large search spaces without exhaustive brute-force search. Their role is often behind the scenes – a user might not see “swarm intelligence” mentioned in a plagiarism report, but swarm methods may have been used to configure the system or perform intermediate tasks like selecting which parts of text to compare closely.

Combining fuzzy logic with swarm intelligence

Fuzzy logic and swarm intelligence can complement each other in plagiarism detection systems. The general pattern is that fuzzy logic handles the decision-making or assessment aspect (dealing with uncertainty in similarity), and swarm intelligence handles an optimisation aspect (searching for the best solution among many possibilities). In a combined fuzzy swarm system, for example, swarm algorithms might optimise the parameters of a fuzzy system or the selection of inputs to the fuzzy system.

For example, one could use PSO to learn the membership function parameters or the rule weights in a fuzzy inference system. If a fuzzy logic model is used to classify text pairs as plagiarised or not, PSO could adjust the shape of the membership curves for “High similarity” or “Low similarity” based on training data, in order to improve accuracy. Conversely, fuzzy logic might guide swarm agents by incorporating human knowledge into the fitness function. A fuzzy fitness function could rate the quality of a candidate solution in a more graded way than a crisp function, helping the swarm converge more efficiently.

For instance, fuzzy swarm logic has been applied to cross-language plagiarism detection [2] and to programming source-code plagiarism [4], demonstrating its versatility. In the following sections, we will explore specific implementations of these ideas to see how effective they have proven to be.

Fuzzy and swarm-based plagiarism detection methods

Fuzzy logic in textual plagiarism detection

One of the straightforward ways fuzzy logic has been applied is by augmenting traditional text plagiarism detectors with fuzzy decision-making. Instead of a binary output, the detector provides a continuous plagiarism score or a categorical rating (like low, medium, high) based on fuzzy rules. Sobowale et al. (2023) developed an automatic text plagiarism detector using fuzzy logic that exemplifies this approach [1].

In their system, the system computes various text similarity features from the documents under comparison. These could include exact word overlap percentages, longest common subsequence length, or even semantic similarity scores. Rather than comparing each feature to a threshold separately, the system feeds them into a fuzzy inference engine. For example, it might use rules such as:

• “IF common-word-overlap is High OR synonym-match is High THEN plagiarism likelihood is High.”
• “IF overlap is Medium AND writing-style-change is Low THEN plagiarism likelihood is Medium.”

and so on.

The outcome is a more refined assessment of plagiarism risk. In testing, this fuzzy logic-based system achieved high detection accuracy [1]. Users found it convenient because they could interpret the output in a nuanced manner – the system might indicate that a document is, say, 70% likely to be plagiarised, which is more informative than a simple yes/no flag. Moreover, by tweaking membership functions and rules, the system can be adapted to different use cases or sensitivity levels. For instance, academic journals could use stricter rules (flagging even moderate similarities as significant), while a high school teacher’s system might be tuned to be more lenient with small overlaps.

The fuzzy logic approach proved effective at reducing false negatives and false positives. It considers multiple indicators simultaneously. A document that just barely fails one threshold but clearly exceeds others can still be flagged. Conversely, a document that has one high similarity metric (perhaps due to common phrases or boilerplate text) but is otherwise dissimilar can avoid being unfairly flagged. This flexibility addresses the rigidity of earlier, rule-bound systems.

Researchers have also noted that fuzzy logic makes plagiarism detectors more user-friendly. In the study by Sobowale et al., the fuzzy-based system was very easy to use and had high functionality as well as accuracy according to user evaluations [1]. The authors did mention that efficiency was only moderate and that future improvements could involve increasing the training data size or incorporating machine learning techniques to fine-tune the fuzzy model [1]. This suggests that while fuzzy logic enhanced accuracy and interpretability, scaling it up or further optimising it could benefit from complementary approaches – possibly even swarm intelligence to automatically adjust the fuzzy rules.

It’s worth noting that fuzzy logic has been embedded in other plagiarism detection tools in subtle ways. For example, Osman et al. (2012) proposed a “fuzzy semantic plagiarism detection” method that assigns a fuzzy similarity value to each phrase and uses those to decide overall plagiarism. Although not widely adopted yet, it highlighted that even semantic judgments (like two sentences conveying the same idea) can be treated in a fuzzy manner. The cumulative lesson from these efforts is that fuzzy logic adds value wherever plagiarism detection requires interpreting shades of grey rather than making black-and-white comparisons.

Cross-language detection using fuzzy swarm summarisation

A pioneering application of fuzzy swarm logic in plagiarism detection targeted the notoriously difficult problem of cross-language plagiarism. In cross-language cases, a plagiarist might translate text from a source in one language into another language. Directly comparing texts in different languages is infeasible without translation or some intermediate representation. Alzahrani et al. addressed this in their 2010 system by employing fuzzy swarm-based document summarisation [2].

The core idea of their approach was to reduce documents to their essential content via summarisation, so that comparing ideas across languages becomes easier and less dependent on exact wording. First, the system summarises both the suspect document (say in Language A) and various candidate source documents (in Language B or a mix of languages). The summarisation itself uses fuzzy logic: each sentence in a document is assigned a score representing how important or central that sentence is to the document’s overall meaning. This scoring might consider features like sentence length, keyword frequency, and position in text (since introductory and concluding sentences often carry key themes). These features are combined in a fuzzy manner – for example, “IF a sentence contains many keywords and is near the beginning, THEN its importance is High.” Instead of a hard cutoff deciding which sentences to keep, every sentence gets a graded importance value.

Next comes the swarm intelligence part. Given the fuzzy importance scores for sentences, the system must decide which subset of sentences will form the summary. An exhaustive search for the optimal subset (maximising coverage of content while minimising length) is combinatorially expensive. Therefore, a Particle Swarm Optimisation algorithm is used. In this context, each “particle” is essentially a candidate summary (a selection of sentences). Its fitness is evaluated based on how well it covers the important content of the document (using the fuzzy importance scores as weights) and how concise it is. The swarm iteratively improves these candidate summaries – particles share information about which sentences seem valuable, somewhat like birds sharing information about food sources. Over iterations, the swarm converges to a near-optimal summary for each document.

Once both the suspect and source documents are summarised (in their respective languages), the system translates one summary into the language of the other for comparison. Alzahrani et al.’s tool used dictionary-based translation, likely translating keywords or key phrases rather than doing a full sentence-by-sentence translation [2]. By focusing on important words and phrases (thanks to the summaries), this translation step became more reliable and less resource-intensive. Finally, with summaries in the same language, a similarity check is performed. If the suspect summary closely matches a source summary (even if the full texts were not direct translations), that indicates cross-language plagiarism.

This fuzzy swarm summarisation approach proved effective in experiments, detecting plagiarism cases that would evade standard detectors. For example, even if a student translated an English article into another language and changed some wording, the main points of their text would likely still mirror the source. The fuzzy summariser would pick out those main points, and the cross-language comparison would catch the alignment. Importantly, because the summarisation is tolerant (using fuzzy scoring), it can handle paraphrasing – two sentences can differ in wording but both be selected for summaries if they convey the same idea.

The success of this system [2] demonstrated several advantages. First, it significantly expanded the scope of plagiarism detection to multilingual contexts, which is crucial in an era of global information sharing. Second, it showcased the power of combining fuzzy and swarm techniques: neither fuzzy logic nor PSO alone would have solved the problem as elegantly. Fuzzy logic provided a nuanced way to evaluate content importance, and swarm optimisation efficiently solved a complex selection problem. Since 2010, cross-language plagiarism detection has advanced further (with new methods using cross-language embeddings and neural machine translation). However, the fuzzy swarm summarisation method remains a creative solution that was ahead of its time in addressing the language barrier in plagiarism.

Context-aware fuzzy clustering for semantic plagiarism

As plagiarism detection techniques evolved, researchers sought to capture not just lexical similarity but also deeper semantic relationships and context. One approach to achieve this is to use clustering methods to group documents or portions of text that discuss similar concepts. Chakrabarty and Roy (2018) proposed a context-aware plagiarism detection framework that relies on agglomerative fuzzy clustering combined with semantic analysis [5].

In their framework, the idea is to represent texts at multiple levels of granularity. They extracted key concepts at the word level (important terms and keywords), at the sentence level (perhaps through sentence embeddings or thematic modelling), and at the paragraph level (the overall topic or theme of each section) [5]. By doing so, they built a multi-scale representation of each document’s content. Two documents could then be compared not just by exact words, but by the concepts they contain and how those concepts are expressed in sentences and paragraphs.

To handle the variety of content (especially when dealing with multi-disciplinary text, as their paper did), fuzzy clustering comes into play. Rather than assigning each document or text segment strictly to one cluster of similar content, fuzzy clustering allows for degrees of membership. For example, an essay on “renewable energy policy” might belong mostly in a cluster about “energy technology” with a high membership value, but also partially in a “government policy” cluster with a lower membership. This reflects the reality that documents can be about multiple topics. In the context-aware framework [5], such fuzzy clustering improved robustness (the system’s consistency in results) because it could handle documents spanning multiple themes without confusion.

They used agglomerative clustering (a bottom-up approach where clusters are gradually merged based on similarity) and introduced an optimisation function to guide the clustering with awareness of context, improving the time complexity [5]. Without diving into the mathematical detail, this likely means they incorporated semantic similarity measures into the clustering algorithm to decide which clusters to merge, and they did so in a way that was more efficient than a naive approach.

By clustering documents (or their pieces) before detailed comparison, the system narrowed down where to look for potential plagiarism. The system would compare the suspect document primarily against those in the same fuzzy cluster(s), rather than against everything. Within those clusters, the detection algorithm looked at concept overlap at the word, sentence, and paragraph level. This context-aware approach could identify plagiarism even if the wording was substantially changed, as long as the underlying ideas and structure were copied. For instance, if someone plagiarised the structure of an argument or the sequence of ideas from a source, this might be caught by paragraph-level concept analysis rather than by exact word matching.

Chakrabarty and Roy reported that their fuzzy clustering-based system outperformed several contemporary techniques in detecting plagiarism, particularly for more subtle cases [5]. The inclusion of semantic features meant that purely paraphrased plagiarism (with low literal overlap) was better detected. And by using fuzzy clustering, the results were more consistent – the system’s performance didn’t vary as wildly across different subject areas or document types. Their experiments showed higher recall (catching more instances of plagiarism) without sacrificing precision (not over-flagging innocent texts), which is the ideal outcome.

In summary, this work [5] shows how fuzzy logic can extend into unsupervised learning techniques like clustering to handle context and semantics. It also implicitly shows another use of optimisation: by improving the clustering algorithm’s efficiency (perhaps using heuristics or constraints), they made it practical on larger datasets. The end result is a more contextually aware plagiarism detection method that aligns well with how human evaluators consider not just the words, but the ideas and the structure of writing when judging plagiarism.

Fuzzy approach to source code plagiarism

Plagiarism is not limited to prose; it is a big concern in programming as well. Students or developers might copy code from each other or from online sources. Source code plagiarism detection has its own challenges, as discussed earlier, because code can be altered in many superficial ways while retaining the core logic. A notable contribution in this realm is a fuzzy-based approach to programming language independent source-code plagiarism detection by Acampora and Cosma (2015) [4].

Traditional code plagiarism detectors often rely on parsing code and comparing abstract syntax trees or program dependency graphs. Those methods can be language-dependent or computationally expensive. Acampora and Cosma took a different route by focusing on a language-agnostic representation and using fuzzy techniques. They tokenised source code files into sequences of tokens (identifiers, keywords, operators, etc.), while stripping away things like variable names and formatting that differ between languages. This way, a loop in Python and a loop in Java, although syntactically different, might produce comparable token patterns.

After this abstraction, their approach employed fuzzy clustering to group similar code submissions together [4]. This is analogous to clustering essays by topic, but here they clustered code by functionality or algorithmic similarity. They used fuzzy c-means clustering, which allows a piece of code to belong to multiple clusters if it exhibits multiple patterns. For example, a piece of code that sorts a list and then prints results might belong strongly in a “sorting algorithms” cluster and more weakly in an “I/O operations” cluster. By using fuzzy clustering, the system didn’t force a single classification on multifaceted code.

Within each cluster, they then used an Adaptive Neuro-Fuzzy Inference System (ANFIS) to decide plagiarism cases [4]. ANFIS is essentially a fuzzy inference system that tunes its own rules or membership functions using neural network training techniques. In practice, they likely extracted features for each pair of code files in a cluster (such as token overlap, structural metrics, or runtime complexity similarity) and fed those into the ANFIS. The ANFIS would output a score or a class indicating whether the two code files should be considered plagiarised. Training this system would involve providing examples of plagiarised code pairs and non-plagiarised pairs for it to learn from.

What makes this approach “swarm” or at least optimisation-oriented is that training an ANFIS or performing clustering can be seen as an optimisation problem. The 2015 paper [4] itself might not explicitly mention a swarm algorithm, but it leverages fuzzy logic and learning to achieve an optimised detection mechanism. One could further improve such a system with evolutionary or swarm-based training techniques – for instance, using a genetic algorithm to optimise initial cluster centres or using PSO to fine-tune the fuzzy rules in ANFIS. Acampora and Cosma reported that their approach outperformed several existing plagiarism detection tools in catching source code plagiarism, and importantly, it worked across different programming languages without requiring any language-specific adjustments [4]. This is a significant benefit. Many programming courses allow students to choose their programming language, and a single tool that can compare a C++ submission to a Java submission (by abstracting both to a common representation) is extremely useful.

The fuzzy source code plagiarism detector is more tolerant of minor differences like changed variable names or added comments, focusing instead on the logic and structure. For instance, two students might implement the same algorithm with different syntax. A purely textual comparison might not flag them. However, a fuzzy logic-based system that considers the presence of similar sequences of operations or similar outputs for given inputs could detect the similarity. Acampora and Cosma’s approach [4] indicates that combining fuzzy clustering with a learned fuzzy inference system provides the flexibility to adapt to different coding styles, and the nuance to distinguish between coincidental similarity and actual plagiarism.

Hybrid models and plagiarism severity assessment

Beyond detecting whether plagiarism has occurred, there is interest in assessing the severity or extent of plagiarism. Not all plagiarism is equal – copying a sentence or two is different from copying an entire chapter. In educational settings, instructors might want to know how serious a case is to decide on penalties. Fuzzy logic, with its notion of degrees, is naturally suited to such assessments.

A recent example is the FTLM model proposed by Sharmila et al. (2024), which stands for Fuzzy TOPSIS Language Modeling [3]. This hybrid approach combines fuzzy logic with a language modelling framework to rank plagiarism severity. Essentially, it borrows from multi-criteria decision-making: TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a method that ranks alternatives based on their distance to an ideal solution. In the plagiarism context, one can imagine multiple “alternatives” (e.g. different suspect documents, or different suspect passages within one document) being ranked by how severely they plagiarise a reference source or a set of sources.

The system likely uses a language model (possibly a TF–IDF vector model or even a modern transformer-based model) to compute similarity scores between the suspect content and source content. These similarity scores could address various aspects: exact overlap, paraphrased similarity, structural similarity, etc. Fuzzy logic comes in by treating these similarity scores as fuzzy criteria. For example, “similarity in facts” might be one criterion, “similarity in phrasing” another – each evaluated with fuzzy values like Low, Medium, High for a given case. The TOPSIS part then takes these fuzzy-evaluated criteria to rank how close each case is to an ideal plagiarised case versus an ideal non-plagiarised case.

Without going into the mathematical details of TOPSIS, the outcome is that FTLM can output a severity ranking or score for a plagiarism case [3]. For instance, a thesis that copied large portions from multiple sources might get a very high severity score, whereas an essay that only mirrors one source in a few paragraphs gets a lower score. This helps in decision support – a committee can quickly see which cases are most egregious and may warrant tougher actions.

The combination of fuzzy sorting (through TOPSIS) with language modelling means the system benefits from both worlds. The language model captures semantic similarity at a fine-grained level, and the fuzzy logic component provides tolerance and gradation when combining those signals. Sharmila et al. reported that this approach was effective in distinguishing levels of plagiarism severity in test scenarios [3]. In effect, it turned plagiarism detection into a ranking problem rather than just a binary classification problem. This is quite useful in large-scale academic settings, where one might have hundreds of submissions and want to prioritise investigating the most severe cases.

Other hybrid models also exist. Some incorporate classical machine learning algorithms (like support vector machines or neural networks) with fuzzy features – these are sometimes called neuro-fuzzy or fuzzy-integrated models. We already saw an example with ANFIS in code plagiarism. In another instance, researchers have combined fuzzy logic with genetic algorithms. For example, a genetic algorithm can evolve the rule set of a fuzzy plagiarism detector to improve it over time, effectively using evolutionary methods to fine-tune a fuzzy system.

There are also purely deep learning-based approaches worth mentioning. Deep neural networks have been trained to detect plagiarism by learning semantic similarity patterns. Some recent systems use transformers (the technology behind BERT or GPT models) to embed sentences from documents into high-dimensional semantic vectors, then compare these vectors to find plagiarism. These deep learning methods can capture paraphrases and even some cross-language similarities if multilingual models are used. They often achieve high accuracy due to their ability to understand language. However, they require large training datasets and computational power, and they act mostly as black boxes – offering less interpretability compared to fuzzy logic systems. By contrast, fuzzy logic models can explain why a piece was flagged (via the rules and intermediate values), which is important when presenting evidence of plagiarism.

In summary, hybrid and advanced models demonstrate that fuzzy swarm logic is not the only approach in town, but it is a highly valuable one. By integrating fuzzy logic with other techniques – such as TOPSIS in FTLM, clustering, or neural networks – researchers have created systems that not only detect plagiarism but also provide richer analysis (like severity levels or context). When compared to purely statistical or deep learning methods, fuzzy-based approaches often bring the benefit of interpretability and the ability to incorporate expert knowledge (like predefined rules or linguistic insights).

Comparison with other approaches

Fuzzy swarm logic methods have shown great promise in detecting complex cases of plagiarism. How do they stack up against other modern approaches? Each methodology has its pros and cons, and understanding these helps in choosing the right tool or designing the next generation of detectors.

Versus traditional methods:

Compared to basic text-matching algorithms (exact matches, string similarity, etc.), fuzzy and swarm-based methods are clearly more powerful in dealing with paraphrased or cross-language plagiarism. Traditional methods are fast and simple, but they often miss any case where text isn’t copied verbatim. Fuzzy logic adds a layer of intelligence by catching partial similarities and by combining multiple evidence sources. Swarm-based optimisation can extend the reach of detection to areas like summarisation and feature tuning that traditional methods wouldn’t handle. The downside might be complexity: fuzzy swarm systems are more complex to implement and might require more computational resources than a simple n-gram checker. However, given today’s computing power and the high cost of missed plagiarism in serious contexts, the trade-off is usually worth it in academic and professional settings.

Versus machine learning and deep learning:

In recent years, researchers have increasingly applied machine learning techniques – particularly deep neural networks – to plagiarism detection. For example, Siamese neural networks can learn to judge if two pieces of text are similar (a common approach for paraphrase detection). Transformer-based models can encode text in ways that capture meaning beyond exact words. These models can be very effective; a well-trained BERT-based model might identify paraphrases or translations with high accuracy. They also have the advantage of improving as more training data becomes available, potentially surpassing fuzzy logic systems which rely on human-crafted rules.

However, deep learning models typically require a lot of training data and can be something of a black box. It’s often not clear why the model flagged a document as plagiarised, which can be a problem when you need to present evidence or justify the result. Fuzzy logic models, by contrast, are more transparent – one can trace which rules fired and see the intermediate similarity values. This transparency is valuable in academic dishonesty cases, where one might face appeals or need to convince a third party of the plagiarism evidence.

Swarm algorithms can actually complement machine learning models rather than compete with them. For instance, a swarm method could optimise the hyperparameters of a deep learning model or help select features for a classical machine learning model. On their own, though, swarm methods aren’t typically used for the direct comparison of text; they’re used around the edges (for optimisation tasks as described).

One area where deep learning clearly shines is in semantic understanding and contextual embeddings. A transformer-based approach can understand that “John F. Kennedy was born in 1917” and “The 35th US President was born in 1917” convey the same fact without overlapping vocabulary. A fuzzy logic approach would need to have rules or a knowledge base to catch that, whereas a learned model might pick it up automatically. That said, a combination is possible: a fuzzy system could take outputs from a neural model as inputs, essentially blending interpretability with raw power.

Versus knowledge-based approaches:

Some plagiarism detectors use external knowledge sources like synonym databases or ontologies. Fuzzy logic can integrate such knowledge easily by having membership functions for “semantic similarity” that use those resources (for example, treating two words as partly similar if an ontology says they are related). Swarm methods are not directly comparable here, but they could optimise how those external resources are used (like weighting certain synonym matches more if they prove useful).

In the literature, approaches like the context-aware fuzzy clustering [5] or the fuzzy summarisation [2] have outperformed baseline methods and even some advanced methods on specific tasks. For example, context-aware fuzzy clustering was superior in dealing with multidisciplinary text collections, where some standard machine learning classifiers might struggle without extensive feature engineering [5]. The fuzzy source code detector [4] outperformed other code plagiarism tools at the time, likely because it combined multiple analysis steps (clustering and inference) in a novel way.

It’s also instructive to consider robustness and adaptability. Fuzzy swarm systems are quite adaptable; if the problem setting changes, you can modify membership functions or re-run the swarm optimisation to tune the system. If tomorrow people start plagiarising using AI-generated text that paraphrases very intelligently, developers could add a fuzzy logic rule to detect AI-style phrasing, for example. By contrast, a machine learning system would need retraining with examples of that phenomenon. Often, an ensemble or hybrid yields the best coverage – for instance, running multiple detectors (some fuzzy, some neural) and combining their outputs.

In practice, many plagiarism detection services use an ensemble of techniques. They might use hashing or “fingerprinting” and direct string matching to quickly narrow down candidate source documents, then apply more sophisticated analysis (like fuzzy semantic similarity or neural embeddings) on those candidates. Swarm optimisation might be used offline to set the thresholds or to combine the scores from these analyses into one final verdict.

Overall, fuzzy swarm logic methods stand out for their ability to handle complex plagiarism scenarios with a high degree of flexibility and interpretability. They may not yet be as widely deployed in commercial tools as simpler methods or the latest deep learning models, but they offer a complementary toolset. For catching certain types of plagiarism – cross-language copying, cleverly paraphrased content, or code plagiarism – they have demonstrated clear advantages. The field is moving fast with AI developments, so ongoing research often looks at integrating fuzzy and swarm ideas with newer techniques to get the best of both worlds.

Challenges and future directions

While fuzzy swarm logic has enriched plagiarism detection, there are still challenges to address and opportunities for further improvement:

Scalability:

Many fuzzy swarm approaches, by their nature, involve more computation than straightforward text matching. Summarising documents with PSO [2] or clustering large datasets with fuzzy algorithms [5] can be resource-intensive. As the volume of digital text grows, ensuring that these methods scale to internet-sized corpora or very large databases is a challenge. Future work could explore ways to make fuzzy inference and swarm optimisation more efficient – possibly by parallelising the algorithms or using approximate methods that cut down computation without too much loss in accuracy. Advances in computing hardware (like GPUs and cloud clusters) and distributed computing frameworks can also help alleviate this issue.

Integration with deep learning:

Rather than viewing fuzzy swarm logic and deep learning as competing paradigms, researchers are investigating how to integrate them. One vision for the future is a plagiarism detection system that uses deep learning to handle the heavy lifting of semantic understanding (for example, generating embeddings for sentences or handling translation via multilingual models) and then uses fuzzy logic to interpret and combine those results in a transparent way. Swarm intelligence could be used to fine-tune the parameters of both components. For instance, PSO might optimise the threshold at which a neural network’s similarity score triggers a plagiarism flag, or even evolve simple rules that catch cases the neural net is uncertain about.

Explainability and user trust:

As plagiarism detectors become more complex (especially with neural networks in the mix), maintaining explainability is crucial. Fuzzy logic inherently provides some explainability because it works with human-readable rules and graded outputs (“similarity is High” etc.). It will be important to keep this as part of the process for user trust. Future tools might provide a summary of why the system flagged a document. For instance, it could say: “50% of content closely matches source X (detected via fuzzy matching of key phrases), and an additional 30% is paraphrased from source Y (detected via semantic embedding comparison).” This kind of explanation draws directly from the fuzzy logic approach of combining evidence. Research in explainable AI (XAI) could influence how fuzzy-swarm-based detectors present their findings to users and administrators.

Adaptive learning:

Plagiarists continually find new ways to avoid detection, which means detectors must adapt. Fuzzy swarm systems can adapt by incorporating learning elements. A future system could have a feedback loop: when it flags something and a human reviewer confirms it was a false alarm or a missed case, the system can adjust itself. This might involve updating a model (like retraining a classifier) or even using an evolutionary algorithm to tweak the fuzzy rules slightly to better fit the confirmed cases. Combining evolutionary algorithms with fuzzy systems points to the idea of a detector that evolves over time as it encounters new types of plagiarism.

Domain-specific plagiarism:

Plagiarism in source code vs. essays vs. mathematical proofs vs. art or music are all different beasts. We saw how researchers tailored fuzzy logic techniques for source code detection by using token patterns and clustering [4]. Future work may extend fuzzy swarm principles to other domains. For example, detecting plagiarism in mathematical writing might require fuzzy logic to handle equivalence of formulas or proofs (a very tricky problem) and swarm algorithms to search through transformations of expressions. Or plagiarism in architectural design might involve comparing patterns in blueprints, where fuzzy logic could measure similarity in spatial layouts. These are speculative ideas, but they illustrate that the core concept – handling uncertainty and optimisation – can be broadly applicable if the right features and representations are used.

Cross-modal plagiarism:

With content now being multi-modal (text, code, images, audio, video), a future challenge is detecting plagiarism across modes. For instance, taking someone’s written article and presenting the ideas as a video or podcast without credit is a form of plagiarism. Fuzzy swarm logic could play a role here by bridging different representations. One can imagine using fuzzy logic to score the conceptual similarity between a video transcript and a text article, and using a swarm algorithm to align sections of spoken content with sections of text. This area is nascent, but it might become more prominent as information is repurposed in various forms.

Human–AI collaboration:

Finally, it’s important to recognise that plagiarism detection tools are aids to human judgment, not replacements. The goal is to bring the most suspicious cases to human attention and provide supporting evidence. Fuzzy logic’s nuanced outputs can assist humans in focusing where it matters (e.g. a document is 85% likely plagiarised from a known source). Going forward, interfaces could be improved so that the system highlights specific passages and explains why they are flagged (using both fuzzy rule rationales and showing the matching source material). Swarm optimisation might even be applied to the user interface – for example, to prioritise which suspected sources to show the user first, based on some optimal ordering.

In conclusion, the challenges for fuzzy swarm plagiarism detection revolve around keeping up with scale and increasingly sophisticated plagiarism methods, while the opportunities lie in combining strengths from multiple AI techniques. The adaptability and interpretability of fuzzy swarm logic ensure that it will remain a valuable approach as the field evolves.

Conclusion

Plagiarism detection has grown from simple string matching to a sophisticated field that incorporates artificial intelligence to tackle nuanced forms of copying. In this paper, we reviewed how fuzzy swarm logic – the combination of fuzzy logic and swarm intelligence – has contributed to this evolution. Fuzzy logic introduces flexibility and human-like reasoning into plagiarism detection, allowing systems to measure similarity in degrees and to make informed judgments even when plagiarism is partial or disguised. Swarm intelligence contributes powerful optimisation techniques, enabling solutions for problems like cross-language comparison and feature selection that would be hard to solve with greedy or brute-force approaches.

We explored a range of applications: from text plagiarism detectors that use fuzzy inference to improve accuracy and user-friendliness [1], to cross-language detection using fuzzy-guided swarm summarisation [2], to context-aware systems that cluster and compare documents using fuzzy semantics [5]. We also saw how fuzzy approaches have been tailored for source code plagiarism [4], proving effective across programming languages, and how hybrid models are assessing plagiarism severity in a graded manner [3]. Across these cases, the common thread is that incorporating fuzziness allows a tolerance for the imprecise nature of human language and plagiarism. Meanwhile, swarm algorithms provide a way to efficiently search for high-similarity alignments or optimal configurations in a vast space of possibilities.

Fuzzy swarm logic methods have shown clear advantages over many conventional methods, particularly in catching paraphrased content and plagiarism that transcends direct textual similarity. They offer a balance between interpretability and performance: stakeholders can understand why a document was flagged (thanks to fuzzy rules and clear criteria), which is often not the case with purely deep learning “black box” models. At the same time, these methods leverage computational intelligence to push detection capabilities further than before – into multilingual domains, into semantic similarity detection, and into structured content like source code.

As plagiarism techniques continue to evolve – with the emergence of AI-generated content and increasingly clever obfuscation – the methods to detect plagiarism must evolve as well. Fuzzy swarm logic provides a framework that is inherently adaptable. We can update rules, add new features to the fuzzy system, and re-optimise swarm algorithms as needed. Therefore, this approach is well-suited to serve as a backbone for future plagiarism detection systems, potentially in tandem with other AI advances.

In closing, fuzzy swarm logic has proven to be more than just an academic idea; practitioners have implemented it in practical tools, demonstrating significant enhancements in plagiarism detection. Whether used standalone or as part of a hybrid system, it adds a layer of intelligent reasoning that keeps detectors a step ahead of plagiarists. By continuing to refine these methods and combine them with emerging technologies, we can hope to maintain academic and creative integrity even in the face of ever more sophisticated plagiarism attempts.

References

1. Sobowale, A., Esan, A., Tomilayo, A., Jooda, B., & Bolajoko, A. (2023). Automatic plagiarism detection using fuzzy-logic. Dutse Journal of Pure and Applied Sciences, 9(3a), 312–318.
2. Alzahrani, S. M., Salim, N., & Abraham, A. (2010). The development of cross-language plagiarism detection tool utilising fuzzy swarm-based summarisation. In 2010 International Conference on Intelligent Systems Design and Applications (pp. 86–90). IEEE.
3. Sharmila, P., Anbananthen, K. S. M., Gunasekaran, N., Balasubramaniam, B., & Deisy, C. (2024). FTLM: A Fuzzy TOPSIS Language Modeling Approach for Plagiarism Severity Assessment. IEEE Access, 12, 122597–122608.
4. Acampora, G., & Cosma, G. (2015). A fuzzy-based approach to programming language independent source-code plagiarism detection. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1–8). IEEE.
5. Chakrabarty, A., & Roy, S. (2018). An efficient context-aware agglomerative fuzzy clustering framework for plagiarism detection. International Journal of Data Mining, Modelling and Management, 10(2), 188–208.