Universal Backdoor Attacks
This addresses a security vulnerability in machine learning systems that rely on large, reused datasets, with incremental improvements in efficiency for backdoor attacks.
The authors tackled the problem of data poisoning in web-scraped datasets for backdooring deep image classifiers, showing that universal attacks can control misclassifications across many classes with minimal poison samples, achieving control over models with up to 6,000 classes while poisoning only 0.15% of the training data.
Web-scraped datasets are vulnerable to data poisoning, which can be used for backdooring deep image classifiers during training. Since training on large datasets is expensive, a model is trained once and re-used many times. Unlike adversarial examples, backdoor attacks often target specific classes rather than any class learned by the model. One might expect that targeting many classes through a naive composition of attacks vastly increases the number of poison samples. We show this is not necessarily true and more efficient, universal data poisoning attacks exist that allow controlling misclassifications from any source class into any target class with a small increase in poison samples. Our idea is to generate triggers with salient characteristics that the model can learn. The triggers we craft exploit a phenomenon we call inter-class poison transferability, where learning a trigger from one class makes the model more vulnerable to learning triggers for other classes. We demonstrate the effectiveness and robustness of our universal backdoor attacks by controlling models with up to 6,000 classes while poisoning only 0.15% of the training dataset. Our source code is available at https://github.com/Ben-Schneider-code/Universal-Backdoor-Attacks.