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.
Machine Translation (MT) has achieved significant progress with the emergence of Transformer-based Neural Machine Translation (NMT) models. However, translating user-generated content (UGC) remains challenging due to the presence of noisy and informal linguistic features such as abbreviations, spelling errors, slang, emojis, code-switching, and inconsistent grammatical structures. These characteristics often degrade translation quality because conventional NMT systems are primarily trained on clean and well-structured corpora. This study proposes an Efficient Dual-BERT Adversarial Network (DBAN) to improve the translation of noisy UGC by integrating contextual representation learning with adversarial training. The proposed framework employs two pretrained BERT encoders to independently learn contextual representations of source and target sentences, while a cross-attention mechanism enhances semantic alignment between both languages. An adversarial discriminator is incorporated to distinguish authentic target representations from generated representations, encouraging the model to learn robust and domain-invariant semantic features. Furthermore, parameter sharing, knowledge distillation, and layer-freezing techniques are introduced to reduce computational complexity without compromising translation performance. The proposed model was evaluated against Standard Transformer NMT and BERT-enhanced NMT using parallel corpora containing informal user-generated text. Translation performance was assessed using Bilingual Evaluation Understudy (BLEU), Metric for Evaluation of Translation with Explicit Ordering (METEOR), Translation Error Rate (TER), and robustness under noisy input conditions. Experimental results demonstrate that the proposed DBAN consistently outperforms the baseline models by producing more accurate translations, preserving semantic meaning more effectively, and exhibiting greater robustness to noisy and domain-diverse user-generated text. The integration of dual contextual encoders and adversarial learning significantly improves contextual understanding and cross-lingual semantic alignment while maintaining computational efficiency. These findings demonstrate that the proposed framework provides a practical and scalable solution for enhancing machine translation of user-generated content and contributes to the development of more robust and context-aware multilingual translation systems suitable for real-world digital communication.
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
Large Language Models (LLMs) are increasingly used in settings where the original user prompt may be unavailable, redacted, or untrusted. This makes response-only adversarial detection an important but challenging problem, because the detector observes only the generated response while the label is determined by the originating prompt. In this study, we investigate response-only adversarial classification of LLM outputs through a comparative evaluation of lexical, contrastive, and contextual representations. Specifically, we compare TF-IDF features, triplet-loss-based metric embeddings, TF-IDF+triplet concatenation, and a Sentence-BERT contextual embedding baseline across three tasks: Prompt Type, Adversarial Type, and Adversarial Class. Experiments on 158 592 response records show that response-only detection is feasible but substantially more difficult than prompt-aware detection. Under stratified group 10-fold evaluation, the strongest TF-IDF+XGBoost baseline achieves 72.37% accuracy for Prompt Type, 82.06% for Adversarial Type, and 56.40% for Adversarial Class. Leave-one-LLM-out evaluation further shows that cross-model generalization remains challenging. Triplet-based metric learning provides model-dependent benefits, particularly for some Adversarial Type configurations, but it does not uniformly outperform strong lexical baselines. Low-dimensional embedding analysis shows that Insertion samples are relatively separable, whereas Prefix and Suffix samples substantially overlap. Overall, the findings provide 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, Ms. Suman· International Journal of Sci...· 0 citations
Existing AI-generated text detectors are vulnerable to attacks that manipulate textual characteristics. In this study, we propose a novel Triospect Detection Framework by using additional perspectives of content (core ideas) and expression (stylistic elements) within a given text. Experiments on two benchmarks involving 17 attacks, 12 domains, and 17 source models demonstrate that Triospect is robust against these attacks. It improves the strong baseline by a significant margin of 22.3% (AUROC) and 13% (TPR01) on the Humanize-16K after-attack subset, and by 9.1% (AUROC) and 22% (TPR01) on the adversarial RAID. This framework marks a pioneering effort in statistical methods to enhance detection reliability against attacks. We release our data and code at https://github.com/baoguangsheng/triospect.
The increasing prevalence of toxic online content has created significant challenges for digital platforms, necessitating the development of automated and robust toxic comment detection systems. Traditional Natural Language Processing (NLP) approaches based on statistical text representations often achieve strong baseline classification performance but remain vulnerable to adversarial text obfuscation techniques designed to evade content moderation systems. This research investigates 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 (BERT) model. Using the Jigsaw Toxic Comment Classification dataset, the problem was simplified into binary toxic versus non-toxic classification. To evaluate robustness, adversarial augmentations including leetspeak substitution, character deletion, and spacing attacks were applied to toxic comments. Experimental results demonstrated that the BERT model significantly outperformed the Logistic Regression baseline in both standard classification performance and resilience against adversarial perturbations. While the Logistic Regression model experienced substantial performance degradation due to its dependence on exact lexical matching, the BERT model maintained strong performance through contextual embeddings and subword tokenization mechanisms. Among the evaluated attacks, substitution based obfuscation produced the greatest reduction in transformer performance, indicating that adversarial NLP remains an ongoing research challenge. The findings highlight the limitations of traditional bag-of-words approaches and 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