Adversarial Attacks on Face Detectors using Neural Net based Constrained\n Optimization

Adversarial attacks involve adding, small, often imperceptible, perturbations\nto inputs with the goal of getting a machine learning model to misclassifying\nthem. While many different adversarial attack strategies have been proposed on\nimage classification models, object detection pipelines have been much harder\nto break. In this paper, we propose a novel strategy to craft adversarial\nexamples by solving a constrained optimization problem using an adversarial\ngenerator network. Our approach is fast and scalable, requiring only a forward\npass through our trained generator network to craft an adversarial sample.\nUnlike in many attack strategies, we show that the same trained generator is\ncapable of attacking new images without explicitly optimizing on them. We\nevaluate our attack on a trained Faster R-CNN face detector on the cropped\n300-W face dataset where we manage to reduce the number of detected faces to\n$0.5\\%$ of all originally detected faces. In a different experiment, also on\n300-W, we demonstrate the robustness of our attack to a JPEG compression based\ndefense typical JPEG compression level of $75\\%$ reduces the effectiveness of\nour attack from only $0.5\\%$ of detected faces to a modest $5.0\\%$.\n

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