Papers › Rethinking Backdoor Attacks

Rethinking Backdoor Attacks

19 Jul 2023arXiv:2307.10163archive 2025-07-28

Alaa Khaddaj, Guillaume Leclerc, Aleksandar Makelov, Kristian Georgiev, Hadi Salman, Andrew Ilyas, Aleksander Madry

In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted examples as outliers in the training set and using techniques from robust statistics to detect and remove them. In this work, we present a different approach to the backdoor attack problem. Specifically, we show that without structural information about the training data distribution, backdoor attacks are indistinguishable from naturally-occurring features in the data--and thus impossible to "detect" in a general sense. Then, guided by this observation, we revisit existing defenses against backdoor attacks and characterize the (often latent) assumptions they make and on which they depend. Finally, we explore an alternative perspective on backdoor attacks: one that assumes these attacks correspond to the strongest feature in the training data. Under this assumption (which we make formal) we develop a new primitive for detecting backdoor attacks. Our primitive naturally gives rise to a detection algorithm that comes with theoretical guarantees and is effective in practice.

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Syntology Ran 6 of 9 code samples harvested from 1 repository linked to this paper; 3 have no recorded run. Of those that ran: 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 4 ran with no contract checked.

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1ran · our draft was wrong
1ran · fixture could not drive it
4ran
3unverified

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LabelSortedDataset SewoongLab/spectre-defense/util.py found in paper text by Syntology ran MIT (permissive) · 75013a384e86bd0d · report
custom_svd SewoongLab/spectre-defense/util.py found in paper text by Syntology ran · fixture could not drive it MIT (permissive) · 9ba46cc13ff798af · report
either_dataloader_dataset_to_both SewoongLab/spectre-defense/util.py found in paper text by Syntology ran MIT (permissive) · dfef8b13029dfa18 · report
get_module_device SewoongLab/spectre-defense/util.py found in paper text by Syntology ran · our draft was wrong MIT (permissive) · d2a1873f0e924783 · report
in_notebook SewoongLab/spectre-defense/util.py found in paper text by Syntology ran · metamorphic tier: deterministic MIT (permissive) · 9761766b17f984f8 · report
make_dataloader SewoongLab/spectre-defense/util.py found in paper text by Syntology ran MIT (permissive) · 63c8b7f7ecc37a2e · report
compute_all_reps SewoongLab/spectre-defense/util.py found in paper text by Syntology unverified MIT (permissive) · 83096e81b6d61f9f · report
compute_spectral_df SewoongLab/spectre-defense/util.py found in paper text by Syntology unverified MIT (permissive) · 77845e534f26e2ba · report
make_pbar SewoongLab/spectre-defense/util.py found in paper text by Syntology unverified MIT (permissive) · be4321ae96632441 · report

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