Generating Adaptive Targeted Adversarial Examples for Content-Based Image Retrieval

Abstract

Massive accessible personal data on the Internet raises the risk of malicious retrieval. In this paper, we propose to conceal the images with the targeted adversarial attacks on content-based image retrieval. An imperceptible perturbation is added to the original image to generate adversarial examples, making the retrieval results similar to the target image but look completely different. Previous work on the targeted attack for image retrieval only introduces a target-specific model and needs to train the model each time for new targets. We extend the attack adaptability by exploiting the target images as conditional input for the generative model. The proposed Adaptive Targeted Attack Generative Adversarial Network (ATA-GAN) is a GAN-based model with a generator and discriminator. The generator extracts the features of origin and target, then uses the Feature Integration Module to explore the relation between the target and original image to ignore the origin feature while paying more attention to the target. Simultaneously, the discriminator distinguishes the realness and ensures the adversarial example is similar to the origin. We evaluate and analyze the performance of the adaptive targeted attack on popular retrieval benchmarks.