Jun 2026· International Joint Conference on Computer Science and Software Engineering· pp. 841-846· 0 citations· 38 references
Abstract
The rapid advancement of Large Language Models (LLMs) such as GPT-4 and Bard has transformed text generation, yet it has also introduced challenges in distinguishing AI-generated content from human-written text. Ensuring authenticity in textual data is crucial in various domains, including academia, journalism, and content moderation. Traditional detection methods often struggle with adversarially manipulated text and paraphrased AI-generated content, limiting their effectiveness in real-world applications. This study proposes a hybrid detection model that integrates DistilBERT and RoBERTa to improve the identification of AI-generated text. The model is evaluated on two publicly available datasets, LLM-Detect AI Generated Text and DAIGT-V3 Train Dataset, achieving accuracy between 94.2% and 96.1%. By incorporating advanced attention mechanisms, multi-task learning, and adversarial training, the model enhances robustness against paraphrased and adversarially manipulated text, improving detection reliability. Additionally, adversarial data augmentation and FGSM-based adversarial training are employed to strengthen the model’s ability to classify text under real-world perturbations. The findings highlight the effectiveness of the proposed approach in mitigating the risks associated with AI-generated content. By leveraging a hybrid transformer-based architecture, the model provides a scalable, adaptable, and efficient solution for detecting AI-generated text across different linguistic and domain-specific contexts.
An Efficient Dual-BERT Adversarial Network (DBAN) is proposed to improve the translation of noisy UGC by integrating contextual representation learning with adversarial training and significantly improves contextual understanding and cross-lingual semantic alignment while maintaining computational efficiency.
A. A. Aliero, Nasiru Muhammad Dankolo· International Journal Of Eng...· 0 citations
Trained on large corpora of image-text pairs, vision-language models (VLMs) have proven broadly useful across many applications. However, they can still make errors that humans rarely do, particularly when exposed to adversarial inputs crafted to mislead them. Traditional approaches to uncovering such vulnerabilities typically optimize a single input, such as a text prompt, to induce incorrect predictions while remaining plausible to human readers. These methods tend to identify only one or a few high-impact adversarial examples, offering a narrow view of model weaknesses. In contrast, we argue that a Quality-Diversity (QD) perspective is more informative. Rather than searching for a single best attack, QD explicitly aims to generate many high-quality adversarial prompts spanning diverse behaviors and characteristics. This allows us not only to diagnose model weaknesses, but also to characterize which prompts are robust and which are especially fragile. Our experiments show that CVT-MAP-Elites, a QD method integrated into our pipeline, discovers a richer and more diverse set of meaningful adversarial samples than quality-only optimization. Consequently, our approach achieves broader search-space coverage and provides deeper insight into VLM failure modes on text-to-image retrieval tasks in both general and medical domains.
Thai Huy Nguyen, Khoa Tran, Quan Minh Phan et al.· Annual Conference on Genetic...· 0 citations
This study investigates response-only adversarial classification of LLM outputs through a comparative evaluation of lexical, contrastive, and contextual representations and provides a realistic benchmark for response-only adversarial detection and clarify the limits of contrastive representations when prompt information is absent.
A. Nasution, Ahmet Emre Ergün, Aytu˘g Onan et al.· IEEE Access· 0 citations
A hybrid detection framework which combines semantically deep embeddings from the RoBERTa transformer with a set of carefully designed language statistics and linguistic statistics and shows excellent resistance to the surface-level adversarial paraphrasing strategy.
Anita Rani, Suman· International Journal of Sci...· 0 citations
This study proposes a novel Triospect Detection Framework by using additional perspectives of content and expression within a given text to enhance detection reliability against attacks.
Investigation of the effectiveness and adversarial robustness of two NLP classification approaches: a traditional TF-IDF Logistic Regression model and a transformer-based Bidirectional Encoder Representations from Transformers model demonstrate the advantages of transformer-based architectures for cybersecurity-oriented content moderation systems operating in adversarial online environments.
Sungho Kim, Mahmoud Matar, Umer Aqeel et al.· International Journal of Gri...· 0 citations