# Towards Visual Distortion in Black-Box Attacks Source: [Towards Visual Distortion in Black-Box Attacks]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:25:36.663Z Authors: Nannan Li, Zhenzhong Chen Published: 2020-07-21T04:42:43.000Z ## Abstract Constructing adversarial examples in a black-box threat model injures the original images by introducing visual distortion. In this paper, we propose a novel black-box attack approach that can directly minimize the induced distortion by learning the noise distribution of the adversarial example, assuming only loss-oracle access to the black-box network. The quantified visual distortion, which measures the perceptual distance between the adversarial example and the original image, is introduced in our loss whilst the gradient of the corresponding non-differentiable loss function is approximated by sampling noise from the learned noise distribution. We validate the effectiveness of our attack on ImageNet. Our attack results in much lower distortion when compared to the state-of-the-art black-box attacks and achieves $100\\%$ success rate on InceptionV3, ResNet50 and VGG16bn. The code is available at https://github.com/Alina-1997/visual-distortion-in-attack. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()