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12 April 2021 CriPI: an efficient critical pixels identification algorithm for fast one-pixel attacks
Wei Quan, Deeraj Nagothu, Nihal Poredi, Yu Chen
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Proceedings Volume 11755, Sensors and Systems for Space Applications XIV; 117550M (2021) https://doi.org/10.1117/12.2581377
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
Deep neural networks (DNN) have been studied intensively in recent years, leading to many practical applications. However, there are also concerns about the security problems and vulnerabilities of DNN. Studies on adversarial network development have shown that relatively more minor perturbations can impact the DNN performance and manipulate its outcome. The impacts of adversarial perturbations have led to the development of advanced techniques for generating image-level perturbations. Once embedded in a clean image, these perturbations are not perceptible to human eyes and fool a well-trained deep learning (DL) convolutional neural network (CNN) classifier. This work introduces a new Critical-Pixel Iterative (CriPI) algorithm after a thorough study on critical pixels’ characteristics. The proposed CriPI algorithm can identify the critical pixels and generate one-pixel attack perturbations with a much higher efficiency. Compared to a one-pixel attack benchmark algorithm, the CriPI algorithm significantly reduces the time delay of the attack from seven minutes to one minute with similar success rates.
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Wei Quan, Deeraj Nagothu, Nihal Poredi, and Yu Chen "CriPI: an efficient critical pixels identification algorithm for fast one-pixel attacks", Proc. SPIE 11755, Sensors and Systems for Space Applications XIV, 117550M (12 April 2021); https://doi.org/10.1117/12.2581377
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KEYWORDS
Convolutional neural networks
Eye models
Image processing
Network security
Neural networks
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