1 citations · 3 across the 8 of their papers we have counts for
12 papers
Mitigating Black-Box Score-Based Query Attacks via Gradient Confusion
Ali Javeri, Javad Hezareh, Radin Shahdaei +1
Score-based black-box adversarial attacks pose a serious threat to deployed machine learning systems by exploiting repeated model queries to craft adversarial examples, requiring n…
TRAP: Tree-Guided Reinforcement-Augmented Probing for Automated LLM Jailbreaking
Mohammad Reza Mirbagheri, Amir Mahdi Sadeghzadeh
Aligned large language models (LLMs) remain vulnerable to jailbreak prompts that circumvent their safety guardrails, making the identification of these vulnerabilities important fo…
Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models
Arian Komaei Koma, Seyed Amir Kasaei, Aida Aryafar +6
Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive retraining. However, we reveal…
PersianAnonymizer: Evaluating LLM-Labeled Training for Efficient NER-based Anonymization in Persian
Mohammad Hossein Shalchian, Mostafa Amiri, Amir Mahdi Sadeghzadeh
We target practical anonymization of Persian customer chats by training a compact NER model from LLM-labeled supervision and selecting the best labeler for deployment. We compare t…
Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks
Hamid Dashtbani, Mehdi Dousti Gandomani, AmirMahdi Sadeghzadeh
Machine learning models are increasingly adapted in various domains. However, adversarial examples pose a significant threat to the reliable deployment of these models. In recent y…
Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models
Arian Komaei Koma, Seyed Amir Kasaei, AmirMahdi Sadeghzadeh +1
Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been introduced to make the model…
Erasure or Erosion? Evaluating Compositional Degradation in Unlearned Text-To-Image Diffusion Models
Arian Komaei Koma, Seyed Amir Kasaei, Ali Aghayari +2
Post-hoc unlearning has emerged as a practical mechanism for removing undesirable concepts from large text-to-image diffusion models. However, prior work primarily evaluates unlear…
EPT Benchmark: Evaluation of Persian Trustworthiness in Large Language Models
Mohammad Reza Mirbagheri, Mohammad Mahdi Mirkamali, Zahra Motoshaker Arani +3
Large Language Models (LLMs), trained on extensive datasets using advanced deep learning architectures, have demonstrated remarkable performance across a wide range of language tas…
Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights
Omid Kargarnovin, Amir Mahdi Sadeghzadeh, Rasool Jalili
With the growing pace of using Deep Learning (DL) to solve various problems, securing these models against adversaries has become one of the main concerns of researchers. Recent st…
HODA: Hardness-Oriented Detection of Model Extraction Attacks
Amir Mahdi Sadeghzadeh, Amir Mohammad Sobhanian, Faezeh Dehghan +1
Model Extraction attacks exploit the target model's prediction API to create a surrogate model in order to steal or reconnoiter the functionality of the target model in the black-b…
AWA: Adversarial Website Adaptation
Amir Mahdi Sadeghzadeh, Behrad Tajali, Rasool Jalili
One of the most important obligations of privacy-enhancing technologies is to bring confidentiality and privacy to users' browsing activities on the Internet. The website fingerpri…
Adversarial Network Traffic: Towards Evaluating the Robustness of Deep Learning-Based Network Traffic Classification
Amir Mahdi Sadeghzadeh, Saeed Shiravi, Rasool Jalili
Network traffic classification is used in various applications such as network traffic management, policy enforcement, and intrusion detection systems. Although most applications e…