Deviations in Representations Induced by Adversarial Attacks
This work addresses the vulnerability of deep learning models to adversarial attacks, which is a critical security issue for practitioners deploying AI systems, but it appears incremental as it focuses on measurement rather than new defense mechanisms.
The paper tackled the problem of understanding how adversarial attacks affect intermediate representations in deep learning models, by presenting a method to measure and analyze deviations across layers, with experiments on CIFAR-10 using various attack algorithms and visualizations.
Deep learning has been a popular topic and has achieved success in many areas. It has drawn the attention of researchers and machine learning practitioners alike, with developed models deployed to a variety of settings. Along with its achievements, research has shown that deep learning models are vulnerable to adversarial attacks. This finding brought about a new direction in research, whereby algorithms were developed to attack and defend vulnerable networks. Our interest is in understanding how these attacks effect change on the intermediate representations of deep learning models. We present a method for measuring and analyzing the deviations in representations induced by adversarial attacks, progressively across a selected set of layers. Experiments are conducted using an assortment of attack algorithms, on the CIFAR-10 dataset, with plots created to visualize the impact of adversarial attacks across different layers in a network.