4 citations · 5 across the 4 of their papers we have counts for
5 papers
Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Vision transformer (ViT) models, when coupled with interpretation models, are regarded as secure and challenging to deceive, making them well-suited for security-critical domains s…
Unveiling Vulnerabilities in Interpretable Deep Learning Systems with Query-Efficient Black-box Attacks
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Deep learning has been rapidly employed in many applications revolutionizing many industries, but it is known to be vulnerable to adversarial attacks. Such attacks pose a serious t…
Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systems
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Deep learning models are susceptible to adversarial samples in white and black-box environments. Although previous studies have shown high attack success rates, coupling DNN models…
Single-Class Target-Specific Attack against Interpretable Deep Learning Systems
Eldor Abdukhamidov, Mohammed Abuhamad, George K. Thiruvathukal +2
In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the…
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo +2
Deep learning methods have gained increased attention in various applications due to their outstanding performance. For exploring how this high performance relates to the proper us…