{"id":20247267,"url":"https://github.com/shreyansh26/convnext-adversarial-examples","last_synced_at":"2026-05-08T22:05:53.257Z","repository":{"id":83435066,"uuid":"448630696","full_name":"shreyansh26/ConvNeXt-Adversarial-Examples","owner":"shreyansh26","description":"Generating Adversarial examples for ConvNeXt","archived":false,"fork":false,"pushed_at":"2022-01-16T18:57:26.000Z","size":7184,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-01-14T02:14:23.172Z","etag":null,"topics":["adversarial-attacks","adversarial-machine-learning","convnext","deeplearning","image-classification"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shreyansh26.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-01-16T17:53:52.000Z","updated_at":"2024-09-22T14:01:21.000Z","dependencies_parsed_at":null,"dependency_job_id":"5eaac432-ca7e-44d9-bbfd-903612b6c996","html_url":"https://github.com/shreyansh26/ConvNeXt-Adversarial-Examples","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/shreyansh26%2FConvNeXt-Adversarial-Examples","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shreyansh26%2FConvNeXt-Adversarial-Examples/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shreyansh26%2FConvNeXt-Adversarial-Examples/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shreyansh26%2FConvNeXt-Adversarial-Examples/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shreyansh26","download_url":"https://codeload.github.com/shreyansh26/ConvNeXt-Adversarial-Examples/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241693180,"owners_count":20004363,"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","adversarial-machine-learning","convnext","deeplearning","image-classification"],"created_at":"2024-11-14T09:36:34.544Z","updated_at":"2025-11-30T22:05:55.306Z","avatar_url":"https://github.com/shreyansh26.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Adversarial examples generation for ConvNeXt\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1c7EiO59cmVbxdCFZRDn82I-gBi6aTPDN?usp=sharing)\n\n**This project is a Pytorch implementation of [@stanislavfort's project](https://twitter.com/stanislavfort/status/1481263565998805002?s=20).**\n\nThe notebook looks at generating adversarial images to \"fool\" the ConvNeXt model's image classification capabilities. [ConvNeXt](https://arxiv.org/abs/2201.03545) came out earlier this year from Meta AI.\n\nThe FGSM (Fast Gradient Sign Method) is a great algorithm to attack models in a *white-box* fashion with the goal of misclassification. Noise is added to the input image (not randomly) but in a manner such that the direction is the same as the gradient of the cost function with respect to the data.\n\nSince this notebook is just the implementation - If you want to know more about FGSM, you may refer these - \n\n1. https://www.tensorflow.org/tutorials/generative/adversarial_fgsm\n2. https://pytorch.org/tutorials/beginner/fgsm_tutorial.html\n3. https://arxiv.org/abs/1412.6572\n\nThe following figure summarizes the goal of this notebook - \n\n\u003cimg src='https://i.imgur.com/C5YKxQ2.png'\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreyansh26%2Fconvnext-adversarial-examples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshreyansh26%2Fconvnext-adversarial-examples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshreyansh26%2Fconvnext-adversarial-examples/lists"}