Attack as the Best Defense: Nullifying Image-to-image Translation GANs via Limit-aware Adversarial Attack

TL;DR

This paper introduces LaS-GSA, a novel black-box adversarial attack method that effectively nullifies image-to-image translation GANs, preventing malicious manipulations with fewer queries and higher success rates.

Contribution

The paper proposes LaS-GSA, a limit-aware, efficient adversarial attack method that cancels img2img translation GANs in black-box settings, with theoretical validation and superior performance.

Findings

LaS-GSA achieves higher success rates than existing methods.

It requires fewer queries to nullify target GANs.

The method effectively prevents malicious image manipulations.

Abstract

With the successful creation of high-quality image-to-image (Img2Img) translation GANs comes the non-ethical applications of DeepFake and DeepNude. Such misuses of img2img techniques present a challenging problem for society. In this work, we tackle the problem by introducing the Limit-Aware Self-Guiding Gradient Sliding Attack (LaS-GSA). LaS-GSA follows the Nullifying Attack to cancel the img2img translation process under a black-box setting. In other words, by processing input images with the proposed LaS-GSA before publishing, any targeted img2img GANs can be nullified, preventing the model from maliciously manipulating the images. To improve efficiency, we introduce the limit-aware random gradient-free estimation and the gradient sliding mechanism to estimate the gradient that adheres to the adversarial limit, i.e., the pixel value limitations of the adversarial example. Theoretical justifications validate how the above techniques prevent inefficiency caused by the adversarial limit in both the direction and the step length. Furthermore, an effective self-guiding prior is extracted solely from the threat model and the target image to efficiently leverage the prior information and guide the gradient estimation process. Extensive experiments demonstrate that LaS-GSA requires fewer queries to nullify the image translation process with higher success rates than 4 state-of-the-art black-box methods.