Effective Ambiguity Attack Against Passport-based DNN Intellectual Property Protection Schemes through Fully Connected Layer Substitution |
➖ |
![arXiv](https://camo.githubusercontent.com/ac7326287158cf0503b4938d50acacb070b8df4c521bb03a3b88e04bb39be2f4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e31313539352d6233316231622e737667) |
➖ |
Progressive Open Space Expansion for Open-Set Model Attribution |
![GitHub](https://camo.githubusercontent.com/3362efa9c1a41c0f1b2b1d49abacc2732c22b137dec7936a5cd4ab744b05e8e2/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4943544d43472f504f53453f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/f77c3b8c2772806259f5e57992d0f6547ed329d939768b7431a070a0ca99d216/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e30363837372d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack |
![GitHub](https://camo.githubusercontent.com/c067ee98d6631f15a166d3c193494e089d7c26c7ad20c61770a49fbeca82c7a1/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f464c4149522d5448552f5061697265644c6f67697473496e76657273696f6e3f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/51111321978a82f9003d3b3d1974e7d9475bf61bb98a18711eb5f8ac978aeb0b/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330342e31313433362d6233316231622e737667) |
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DartBlur: Privacy Preservation with Detection Artifact Suppression |
![GitHub](https://camo.githubusercontent.com/41462915342a5c316ef01edbb647caa404eb593c5358255636384b6ce44e9c79/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4a614e67323333332f44617274426c75723f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Reinforcement Learning-based Black-Box Model Inversion Attacks |
![GitHub](https://camo.githubusercontent.com/4e44601f892dfbcf2b76ed5e9e11b150cbc5e2d6829d789ca623fce5cf8a5498/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f48616e47796f6a696e2f524c422d4d493f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/5ea11fdb65563c9bada009223ea0d94dc4786343affd88501b47544d3766f460/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330342e30343632352d6233316231622e737667) |
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Model-Agnostic Gender Debiased Image Captioning |
![GitHub](https://camo.githubusercontent.com/24bb063197a984e85a45d8813abe1c69068e6d4d05fabdb313b681eb10f49a4a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7265626e656a2f4c494252413f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/3ce5c3557d1b5cb51e032f2be17c889211db5cbeebd0347fec159ba3b489c589/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330342e30333639332d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Uncurated Image-Text Datasets: Shedding Light on Demographic Bias ![CVPR - Highlight](https://camo.githubusercontent.com/5cb496e2b9e24dbb9469b9fdf6f120a9d249de14965ea65e1c41199f309ceac9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f435650522d486967686c696768742d464646463030) |
![GitHub](https://camo.githubusercontent.com/f8692ed277c6853bec36a2aa839d5baeaefaac1a983363af925b5f749c04cb6e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6e6f616761726369612f70686173653f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/e0d6dab2ca7b1e58b441ac6ce14d739e3bdd33f6f5d9ffc745b74544a481f0eb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330342e30323832382d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
AltFreezing for more General Video Face Forgery Detection ![CVPR - Highlight](https://camo.githubusercontent.com/5cb496e2b9e24dbb9469b9fdf6f120a9d249de14965ea65e1c41199f309ceac9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f435650522d486967686c696768742d464646463030) |
![GitHub](https://camo.githubusercontent.com/fd3bcf34b322b521ff0bf75c4f9a209be66f07ad16dc818b4634d3a9e1d9b61a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5a68656e646f6e6757616e67362f416c74467265657a696e673f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/498f73b6bbd5504f17ff1972feae9c657aceb555edadc8b43e0205290b8baefe/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330372e30383331372d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Make Landscape Flatter in Differentially Private Federated Learning |
![GitHub](https://camo.githubusercontent.com/db1dfb9419194f8a4b9e4ec8bcf35d84bcbab90468311656fed335cc5954c979/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f594d4a532d497266616e2f44502d46656453414d3f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/4e4dd40840bcf68c2d2e558eae604b2505acc6c10610fc3c9b3700fa3669dd96/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e31313234322d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
DynaFed: Tackling Client Data Heterogeneity with Global Dynamics |
![GitHub](https://camo.githubusercontent.com/8d40e9990740a57c9520afb4ab4780201c7e14b7fbf2638f89b2a47b550d0c0f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f706970696c75726a2f44796e614665643f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/e25f4e72ded1cf8e4a48cdaa1a005e58c90d51e85178965d9dbceb1144832db9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323231312e31303837382d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Re-Thinking Model Inversion Attacks Against Deep Neural Networks |
![GitHub](https://camo.githubusercontent.com/b20a4c55dc51ea66da5563b78741025989cd0ee676a9ded30bd69e7d427eb50e/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f737574642d76697375616c2d636f6d707574696e672d67726f75702f52652d7468696e6b696e675f4d493f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/c04e3d24d818ffefc1eb5e6e0f91bd543d72ba0e8eb727e194f87dafef894dec/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330342e30313636392d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models |
![GitHub Page](https://camo.githubusercontent.com/1d553b1d08bbbe375bd4742184674434cc4f6ca75c8293238dcc7d5b6202674a/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4769744875622d506167652d3135393935372e737667) |
![arXiv](https://camo.githubusercontent.com/8e4598b05775b49f2e36700bf510c1bf4ed03996becdea25d0116c91588f77bb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323231312e30353130352d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
TrojViT: Trojan Insertion in Vision Transformers |
![GitHub](https://camo.githubusercontent.com/5a3df28b51fbe5bd6644207af4e6e1550215d10a2b5edd446d8526df8694b8b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6d787a68656e672f54726f6a5669543f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/9a4f99507cb015984f35119507b08a06a5620f0ad30bbc8be1120954d629d191/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323230382e31333034392d6233316231622e737667) |
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Difficulty-based Sampling for Debiased Contrastive Representation Learning |
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![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection |
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![arXiv](https://camo.githubusercontent.com/b2f92ed363310bd08fca087dbf036c47356a45b6e6804402bcc7fa8062e45580/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e31313037382d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Fair Scratch Tickets: Finding Fair Sparse Networks without Weight Training |
![GitHub](https://camo.githubusercontent.com/7cd50a55c5fbe077bdaf0abe173f8f5614875f5356fcf71d38a1e95a2c891644/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f48756e676572505741592f466169722d536372617463682d5469636b6574733f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
CLIP2Protect: Protecting Facial Privacy using Text-Guided Makeup via Adversarial Latent Search |
![GitHub](https://camo.githubusercontent.com/fca986b2fd790176f88195dbabe26f3a3487e857b8c74f0822e69b3bbf591cdd/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f66616861647368616d736861642f436c69703250726f746563743f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures |
![GitHub](https://camo.githubusercontent.com/75a8a92d4db5ed7112aa07889c5ad676e184084296aba64ce78416517353152a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f4953542d4441534c61622f7072756e65642d766973696f6e2d6d6f64656c2d626961733f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/a7ebc23f5f81af2940748432ec2e1eaf5eb88e291104aa480209e4d301249b33/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330342e31323632322d6233316231622e737667) |
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Learning to Generate Image Embeddings with User-Level Differential Privacy |
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![arXiv](https://camo.githubusercontent.com/730c91c0b2f626db086e8d60c3b2f381a7cb9b226016925dc62279ad5446f6ea/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323231312e31303834342d6233316231622e737667) |
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Bias Mimicking: A Simple Sampling Approach for Bias Mitigation |
![GitHub](https://camo.githubusercontent.com/c4c6dd24fa0039ceed0468e58a8557aeddc93331eb0439f1688d72742cb9512f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6d7172616974656d2f426961732d4d696d69636b696e673f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/8175a604e4fe74a0717a1928ade2db9b81c578877f718f23f9fb7ef8e86c0f23/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323230392e31353630352d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-Friendly Distillation Loss |
![GitHub](https://camo.githubusercontent.com/7f4ea5b47889ca4c4e97ff430a206da0de2c5253eafebe006d0ec8976f2c0de4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f746e757262656b2f636170726964652d6c6561726e696e673f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
DeAR: Debiasing Vision-Language Models with Additive Residuals |
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![arXiv](https://camo.githubusercontent.com/f461edd7bf49b4b804d6360ed687d2622c7aaa2a87f63b428c5db60f7dff6720/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e31303433312d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Deep Deterministic Uncertainty: A New Simple Baseline ![CVPR - Highlight](https://camo.githubusercontent.com/5cb496e2b9e24dbb9469b9fdf6f120a9d249de14965ea65e1c41199f309ceac9/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f435650522d486967686c696768742d464646463030) |
![GitHub](https://camo.githubusercontent.com/cfb7060bcfa472d893a96c77bb3ca64ca2c2ad123430bd896a6d1c79a303f310/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6f6d656761667261676765722f4444553f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Manipulating Transfer Learning for Property Inference |
![GitHub](https://camo.githubusercontent.com/b2543716d8158531e7b1f312826d63579982735ef79796eeba401bab24be60f9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f79756c6f6e677432332f5472616e736665722d496e666572656e63653f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/b9a16348078c91a06917218f5c44227dc32ba4cf07158f53de9f09b2cdebaf53/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e31313634332d6233316231622e737667) |
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Training Debiased Subnetworks with Contrastive Weight Pruning |
![GitHub](https://camo.githubusercontent.com/a39a3028c175ac93d5fa2e90ec5a52298dbb09fc99ff25869418c6bbb2da2e07/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5061726b47656f6e59656f6e672f444357503f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/090419794397424148f14835775243d04348c720899660e74e7b10f18c9d1ec4/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323231302e30353234372d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models |
![GitHub](https://camo.githubusercontent.com/ffcbdd8b2f0e0c7b5bcebdf48a9f25dd1f43f9264674b1008a6604fe4a8f8345/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f736f6d657061676f2f4443523f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/fd95fdcda00e179f54511cf301e86b628339db4fde3055779c22091a461f3229/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323231322e30333836302d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
STDLens: Model Hijacking-Resilient Federated Learning for Object Detection |
![GitHub](https://camo.githubusercontent.com/a5a7e8ae5f5c57d3e44be5a55793fbfdd4aa26edfd3e7d18b81ba1b37dd0a349/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6769742d6469736c2f5354444c656e733f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/f9d550a1305db08fb6ab877891a9c51ed4165f55a0e0260a165686b419892293/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323330332e31313531312d6233316231622e737667) |
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Architectural Backdoors in Neural Networks |
![GitHub](https://camo.githubusercontent.com/58c22b2dacfe1821621c12a1d205141e2d92749a296bf86325aaba41c55579cf/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5175616e674e677579656e323630392f4152434849544543545552414c2d4241434b444f4f52532d494e2d4e455552414c2d4e4554574f524b533f7374796c653d666c6174) |
![arXiv](https://camo.githubusercontent.com/c376d0e947720713d15a14d4d60aad76b3c4f931a370421bdde9dcac5f05be14/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f61725869762d323230362e30373834302d6233316231622e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
MEDIC: Remove Model Backdoors via Importance Driven Cloning |
![GitHub](https://camo.githubusercontent.com/38316dd6e030329ab866590c37b75c8cc8ade8e5859b5741631617a1a01f89c9/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f7169756c696e6778752f4d454449433f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |
Learning Debiased Representations via Conditional Attribute Interpolation |
![GitHub](https://camo.githubusercontent.com/829c66662846b472904c745a91c5df0492f90c2bfe8cf237ae14611aa91b9cb4/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f5a68616e6759696b6169692f6368692d7371756172653f7374796c653d666c6174) |
![thecvf](https://camo.githubusercontent.com/570147fbf0c4aade041dc1e76b9cc9cc802ebdef0c7e20c98df264ef7919b215/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f7064662d7468656376662d3733393543352e737667) |
![YouTube](https://camo.githubusercontent.com/4c91b5d587f82e3ea0df6978845aea9370200c5f626a2fa7d6e48d2db40c8aa0/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f596f75547562652d2532334646303030302e7376673f7374796c653d666f722d7468652d6261646765266c6f676f3d596f7554756265266c6f676f436f6c6f723d7768697465) |