21 citations · 29 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
SHIELD: Thwarting Code Authorship Attribution
Mohammed Abuhamad, Changhun Jung, David Mohaisen +1
Authorship attribution has become increasingly accurate, posing a serious privacy risk for programmers who wish to remain anonymous. In this paper, we introduce SHIELD to examine t…