An Adversarial Example Generation Algorithm Based on DE-C&W

Security issues surrounding deep learning models weaken their application effectiveness in various fields. Studying attacks against deep learning models contributes to evaluating their security and improving it in a targeted manner. Among the methods used for this purpose, adversarial example generation methods for deep learning models have become a hot topic in academic research. To overcome problems such as extensive network access, high attack costs, and limited universality in generating adversarial examples, this paper proposes a generic algorithm for adversarial example generation based on improved DE-C&W. The algorithm employs an improved differential evolution (DE) algorithm to conduct a global search of the original examples, searching for vulnerable sensitive points susceptible to being attacked. Then, random perturbations are added to these sensitive points to obtain adversarial examples, which are used as the initial input of C&W attack. The loss functions of the C&W attack algorithm are constructed based on these initial input examples, and the loss function is further optimized using the Adaptive Moment Estimation (Adam) algorithm to obtain the optimal perturbation vector. The experimental results demonstrate that the algorithm not only ensures that the generated adversarial examples achieve a higher success rate of attacks, but also exhibits better transferability while reducing the average number of queries and lowering attack costs.