Hong-Han Shuai, Ming-Syan Chen, Hsi-Wen Chen, De-Nian Yang, Chin-Yuan Yeh
We lifted 5 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jimmy-academia/lasgsa | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| get_norm_layer | Ran | jimmy-academia/lasgsa/src/networks.py code served (permissive licence) · get_code("b27dffc0609fbec0") |
| get_scheduler | Ran | jimmy-academia/lasgsa/src/networks.py code served (permissive licence) · get_code("f28f371ac79d4e8a") |
| init_net | Ran | jimmy-academia/lasgsa/src/networks.py code served (permissive licence) · get_code("8dae8fea47c19e0f") |
| make_define_dir | Ran | jimmy-academia/lasgsa/src/utils.py code served (permissive licence) · get_code("e531c967ad1b253a") |
| make_imageloader | Ran | jimmy-academia/lasgsa/src/utils.py code served (permissive licence) · get_code("aec2024b9a4769fa") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Due to the great success of image-to-image (Img2Img) translation GANs, many applications with ethics issues arise, e.g., DeepFake and DeepNude, presenting a challenging problem to prevent the misuse of these techniques. In this work, we tackle the problem by a new adversarial attack scheme, namely the Nullifying Attack, which cancels the image translation process and proposes a corresponding framework, the Limit-Aware Self-Guiding Gradient Sliding Attack (LaS-GSA) under a black-box setting. In other words, by processing the image with the proposed LaS-GSA before publishing, any image translation functions can be nullified, which prevents the images from malicious manipulations. First, we introduce the limit-aware RGF 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. We theoretically prove that our model is able to avoid the error caused by the projection in both the direction and the length. Then, 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 methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.02516")
get_code_for_paper("2110.02516")
have("2110.02516")
Connect an agent — have() is free.