  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.
