Mr. Wei Quan Profile

Wei Quan

at Binghamton Univ

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Proceedings Article | 12 April 2021 Presentation + Paper

CriPI: an efficient critical pixels identification algorithm for fast one-pixel attacks

Wei Quan, Deeraj Nagothu, Nihal Poredi, Yu Chen

Proceedings Volume 11755, 117550M (2021) https://doi.org/10.1117/12.2581377

KEYWORDS: Neural networks, Network security, Image processing, Eye models, Convolutional neural networks

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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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