Authors: Egor Kovalev, Georgii Bychkov, Khaled Abud, Aleksandr Gushchin, Anna Chistyakova, Sergey Lavrushkin, Dmitriy Vatolin, Anastasia Antsiferova
Abstract: Adversarial robustness of neural networks is an increasingly important area
of research, combining studies on computer vision models, large language models
(LLMs), and others. With the release of JPEG AI – the first standard for
end-to-end neural image compression (NIC) methods – the question of its
robustness has become critically significant. JPEG AI is among the first
international, real-world applications of neural-network-based models to be
embedded in consumer devices. However, research on NIC robustness has been
limited to open-source codecs and a narrow range of attacks. This paper
proposes a new methodology for measuring NIC robustness to adversarial attacks.
We present the first large-scale evaluation of JPEG AI’s robustness, comparing
it with other NIC models. Our evaluation results and code are publicly
available online (link is hidden for a blind review).
Source: http://arxiv.org/abs/2411.11795v1
