{"id":13561644,"url":"https://github.com/Harry24k/CW-pytorch","last_synced_at":"2025-04-03T17:31:19.009Z","repository":{"id":104396822,"uuid":"180731101","full_name":"Harry24k/CW-pytorch","owner":"Harry24k","description":"A pytorch implementation of \"Towards Evaluating the Robustness of Neural Networks\"","archived":false,"fork":false,"pushed_at":"2019-09-04T14:46:36.000Z","size":942,"stargazers_count":56,"open_issues_count":2,"forks_count":12,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-24T15:41:58.745Z","etag":null,"topics":["adversarial-attacks","deep-learning","pytorch"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Harry24k.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-04-11T06:43:54.000Z","updated_at":"2025-02-28T03:31:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"5cdc0395-f4bc-44ba-a612-51e686ce627f","html_url":"https://github.com/Harry24k/CW-pytorch","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FCW-pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FCW-pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FCW-pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Harry24k%2FCW-pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Harry24k","download_url":"https://codeload.github.com/Harry24k/CW-pytorch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247046916,"owners_count":20874739,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["adversarial-attacks","deep-learning","pytorch"],"created_at":"2024-08-01T13:00:59.504Z","updated_at":"2025-04-03T17:31:17.097Z","avatar_url":"https://github.com/Harry24k.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# CW-pytorch\n**A pytorch implementation of \"[Towards Evaluating the Robustness of Neural Networks](https://arxiv.org/abs/1608.04644)\"**\n\n## Summary\nThis code is a pytorch implementation of **CW attack**.   \nIn this code, I used above methods to fool [Inception v3](https://arxiv.org/abs/1512.00567).   \n'[Giant Panda](http://www.image-net.org/)' used for an example.   \nYou can add other pictures with a folder with the label name in the 'data/imagenet'.    \n\n## Requirements\n* python==3.6   \n* numpy==1.14.2   \n* pytorch==1.0.1   \n\n## Important results not in the code\n- This paper suggested new approach to the adversarial attack.(p.6-7)\n    - Compared 7 new objective functions(*f*) for generating adversarial images.\n    - Used *tanh* for solving box constraints.\n- Three new attack algorithms proposed.(p.9-10)\n    - They are L_2, L_0, L_inf. (Among these, L_2 is implementd in this code)\n- These attacks made the [defensive distillation](https://arxiv.org/abs/1511.04508) helpless.(p.12-14)\n    - All of new attack methods succeed with 100% success.\n\n## Notice\n- This Repository won't be updated.\n- Please check [the package of adversarial attacks in pytorch](https://github.com/Harry24k/adversairal-attacks-pytorch)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FHarry24k%2FCW-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FHarry24k%2FCW-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FHarry24k%2FCW-pytorch/lists"}