3260 papers • 126 benchmarks • 313 datasets
Backdoor attacks inject maliciously constructed data into a training set so that, at test time, the trained model misclassifies inputs patched with a backdoor trigger as an adversarially-desired target class.
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This work proposes a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time.
Refool is proposed, a new type of backdoor attack inspired by an important natural phenomenon: reflection to plant reflections as backdoor into a victim model and can attack state-of-the-art DNNs with high success rate, and is resistant to state of theart backdoor defenses.
A novel Frequency-Injection based Backdoor Attack method (FIBA) that is capable of delivering attacks in various MIA tasks, and preserves the semantics of the poisoned image pixels, and can perform attacks on both classification and dense prediction models.
This work proposes BadEncoder, the first backdoor attack to self-supervised learning, which injects backdoors into a pre-trained image encoder such that the downstream classifiers built based on the backdoored imageEncoder for different downstream tasks simultaneously inherit the backdoor behavior.
It is suggested that SSL and supervised learning are comparably vulnerable to backdoor attacks, and the existing defenses against supervised backdoor attacks are not easily retrofitted to the unique vulnerability of SSL.
This work proposes a subgraph based backdoor attack to GNN for graph classification that predicts an attacker-chosen target label for a testing graph once a predefined subgraph is injected to the testing graph.
The embedding and extraction of knowledge in tree ensemble classifiers is studied, and an algorithm to extract the embedded knowledge is developed, by reducing the problem to be solvable with an SMT (satisfiability modulo theories) solver.
A simple and effective textual backdoor defense named ONION, which is based on outlier word detection and, to the best of the knowledge, is the first method that can handle all the textual backdoor attack situations.
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