TY - JOUR
T1 - A Novel Dynamic Programming Approach for Generating Sparse Adversarial Examples in Deep Neural Networks
AU - Fatima El Idrissi
AU - Samira Bennani
AU - Youssef Benali
AU - Ahmed Ben Ali
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2024
VL - 3
IS - 2
SP - 50
N2 - Recent investigations have demonstrated that deep neural networks are susceptible to subtly modified input data. This paper introduces a novel untargeted white-box adversarial attack termed the dynamic programming-based sub-pixel score method (SPSM) attack (DPSPSM). This technique modifies conventional gradient-based white-box adversarial methods, which are typically restricted by a predetermined Hamming distance, by incorporating a dynamic programming framework. The method is driven by a pixel score metric approach, the SPSM, which is developed herein. Unlike traditional gradient-based adversarial attacks that introduce nearly imperceptible modifications across the entire image, DPSPSM operates rapidly and maintains robustness by altering only a restricted number of input pixels. The introduced algorithm quantizes the gradient updates using a score computed for every pixel, which aggregates the contributions across all channels. Experimental outcomes indicate that the DPSPSM successfully misleads the model, achieving a success rate of 30.45% on the CIFAR-10 test set and 29.30% on the CIFAR-100 test set.
UR - https://tipublications.com/g980038473
ER -