A General Adversarial Attack Method Based on Random Gradient Ascent and Spherical Projection

In general adversarial attacks oriented to sample sets, the general perturbation design that causes most sample to output errors is the key to the research. This paper takes the typical convolutional neural networks as the research object, summarizes the existing general perturbation generation algorithms, and proposes a general perturbation generation algorithm that combines batch random gradient ascent and spherical projection search. In each iteration of the algorithm, a small batch of samples are extracted from the sample set, and the general perturbation is calculated by using the random gradient rising strategy which reduces the value of the loss function. The general perturbation is then projected to the high-dimensional spherical surface with a radius of ε, so as to reduce the search space of general disturbances. The algorithm also introduces a regularization technique to improve the generation quality of general disturbances. Experimental results show that compared with the baseline algorithm, the attack success rate is significantly increased, and the solution efficiency of general perturbation is improved by about 30 times. © 2022, Editorial Department of Journal of Northeastern University. All right reserved.