  Training state-of-the-art (SOTA) deep learning models requires a large amount
of data. The visual information present in the training data can be misused,
which creates a huge privacy concern. One of the prominent solutions for this
issue is perceptual encryption, which converts images into an unrecognizable
format to protect the sensitive visual information in the training data. This
comes at the cost of a significant reduction in the accuracy of the models.
Adversarial Visual Information Hiding (AV IH) overcomes this drawback to
protect image privacy by attempting to create encrypted images that are
unrecognizable to the human eye while keeping relevant features for the target
model. In this paper, we introduce the Attack GAN (AGAN ) method, a new
Generative Adversarial Network (GAN )-based attack that exposes multiple
vulnerabilities in the AV IH method. To show the adaptability, the AGAN is
extended to traditional perceptual encryption methods of Learnable encryption
(LE) and Encryption-then-Compression (EtC). Extensive experiments were
conducted on diverse image datasets and target models to validate the efficacy
of our AGAN method. The results show that AGAN can successfully break
perceptual encryption methods by reconstructing original images from their AV
IH encrypted images. AGAN can be used as a benchmark tool to evaluate the
robustness of encryption methods for privacy protection such as AV IH.
