Revisiting Role of Autoencoders in Adversarial Settings
This work addresses adversarial attacks in machine learning, but it appears incremental as it revisits and analyzes existing autoencoder structures without introducing a new method.
The paper investigates the role of autoencoders in adversarial settings, finding that they possess inherent adversarial robustness and may use robust features, which could inform future defense research.
To combat against adversarial attacks, autoencoder structure is widely used to perform denoising which is regarded as gradient masking. In this paper, we revisit the role of autoencoders in adversarial settings. Through the comprehensive experimental results and analysis, this paper presents the inherent property of adversarial robustness in the autoencoders. We also found that autoencoders may use robust features that cause inherent adversarial robustness. We believe that our discovery of the adversarial robustness of the autoencoders can provide clues to the future research and applications for adversarial defense.