Jul 2026· 2026 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)· pp. 664-669· 0 citations· 29 references
Abstract
The rapid adoption of large language models has urged the development of reliable detectors that are capable of distinguishing AI-generated text from human-written content. While recent Transformer-based detectors have shown promising performance, their robustness against adversarial manipulation remains underexplored, particularly in multilingual and cross-domain settings. This study investigates the vulnerability of an IndoBERT-based AI-generated text detector to synonym substitution attacks guided by Integrated Gradients (IG) that identify words most influential to the model’s predictions. By leveraging IG to selectively perturb high-importance tokens, we construct a constrained synonym substitution attack that aims to evade detection while preserving semantic fidelity. Experiments are conducted on Indonesian news articles and speech transcripts to assess domain-specific robustness. The results reveal that attribution-guided attacks can significantly degrade detector performance, achieving meaningful attack success rates on AI-generated texts that were initially classified correctly. Moreover, the noticeable cross-domain behaviors are also observed. Where speech texts are more vulnerable to meaning-preserving perturbations but require substantially higher attack effort, whereas news texts demand fewer attempts at the cost of higher lexical modification. Overall, this work proves that strong pre-attack accuracy does not guarantee the model’s resilience against guided adversarial attacks and emphasizes the importance of incorporating explainability-driven adversarial analysis in the development of future detection systems.
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
The rapid spread of AI-generated misinformation poses serious risks to public discourse and societal stability, creating an urgent need for automated fake text detection systems. This paper presents an authenticity detection framework that integrates transformer-based architectures (RoBERTa and BERT) with parameter-efficient fine-tuning strategies, specifically DoRA and LoRA, evaluated on the FakeNewsNet dataset. The proposed RoBERTa + DoRA model achieves 88.40% accuracy on FakeNewsNet while updating less than 1% of model parameters, outperforming full fine-tuning baselines and demonstrating superior computational efficiency. Ensemble strategies further boost performance, with a hard voting ensemble reaching 92.23% accuracy. Compared to resource-intensive large language models such as ChatGPT and GPT-4, the proposed approach delivers competitive results with significantly lower computational overhead, making it suitable for real-time deployment. These findings demonstrate that modern parameter-efficient methods, when combined with modular architectures, offer a scalable and adaptable solution for fake news detection across diverse domains.
A. Abdelhady· 2026 6th International Confe...· 0 citations
D-SCAN (Document-level Signal Collapse Analysis), a lightweight detection framework that monitors attention dynamics to identify attacked generations and proposes D-SCAN, a lightweight detection framework that can detect attacks even when they fail to alter the final answer.
Yingtao Ren, Ziyi Zhao, Yiwei Fu et al.· Annual International ACM SIG...· 0 citations
This work investigates the impact of cross-lingual prompt injection attacks on LLM-based relevance judgments using TREC Deep Learning collections and two open-weight models under established prompting frameworks, and demonstrates that multilingual query-based injections are highly effective in inflating relevance scores while simultaneously evading existing prompt-injection defenses.
Fake news detection has become a major research topic at the intersection of artificial intelligence, data mining, and information security. In this paper, we evaluate the performance of English-trained algorithms on English translations of Romanian-sourced news articles, using a translation-mediated cross-domain evaluation design. The study is based on source code generated with the assistance of artificial intelligence systems for a set of machine learning and transformer-based models. The code was subsequently implemented in Google Colab. 2026, trained on international benchmark datasets, and tested on Romanian news content. This design allowed the rapid prototyping of multiple detection pipelines and the systematic observation of their behavior in a media environment different from that represented in the training corpora. The models were evaluated comparatively using standard classification metrics, including accuracy, precision, recall, and F1-score, complemented by additional indicators relevant to model robustness and practical usability. The experimental results revealed significant differences in performance across algorithms when applied to English translations of Romanian-language news content after training on international datasets. However, this study does not provide a direct comparison between model performance on the international benchmark datasets and the Romanian test corpus; therefore, the gap between the international training corpus and the Romanian-sourced test corpus is interpreted as an exploratory limitation and as a direction for future research. Based on these findings, we propose an empirical classification of the tested models according to their predictive effectiveness, their contextual robustness across linguistic environments, and their operational relevance as filtering tools for institutional monitoring. The results show that AI-assisted coding workflows can provide a viable starting point for reproducible misinformation research, but they also underline the limitations of directly transferring models trained on non-Romanian data to local media ecosystems. The study offers both a replicable evaluation framework and practical insights for institutions involved in strategic communication, public security, and the monitoring of information threats.
C. Coman, Costel Marian Dalban, Vlad Bătrânu-Pințea et al.· Information· 0 citations
Human evaluators struggle to distinguish original scientific abstracts from AI-generated text, as AI-produced formal language appears neat and convincing; prior studies report reviewers correctly identify only 68% of AI-generated abstracts while misclassifying 14% of human texts. This study presents an exploratory, generator-specific evaluation of mDeBERTa v3 using zero-shot Natural Language Inference (NLI) classification, applied to Indonesian scientific abstracts synthesized via IndoT5-base-paraphrase rather than AI-generated text in general. A balanced 2,274-abstract dataset paired human abstracts (SINTA 3 journals) with IndoT5-base-paraphrase outputs as the AI class. Mann-Whitney U analysis on seven linguistic features revealed significant differences (p < 0.001) across all. A critical anomaly emerged: AI texts showed higher sentence-length variation (SD = 15.44) than human texts (SD = 7.99), contradicting the assumption that AI text is more uniform, attributable to context-window exhaustion in IndoT5 producing semantic hallucinations when synthesizing dense abstracts. Testing three NLI scenarios showed a single instruction targeting this fluctuation achieved the highest Recall (76.52%) but with 790 false positives among 1,137 human abstracts, limiting accuracy to 53.52%; added complexity further degraded AI-class recall due to vocabulary overlap. A Random Forest classifier trained on the same features achieved 91.21% accuracy (F1 = 0.9130), substantially outperforming the zero-shot approach and confirming the anomaly as a strong, learnable signal. These results indicate zero-shot NLI can partially track a generator's mechanical artifacts through a single targeted instruction, but remains insufficiently precise to separate machine-error fluctuation from natural human variation, and is not recommended for standalone academic-integrity screening without further refinement
Aldo Syahputra, Aris Wahyu Murdiyanto, Ulfi Saidata Aesyi· Indonesian Journal of Data a...· 0 citations