The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices. (arXiv:2209.11715v1 [cs.CR])

Deep Neural Networks (DNNs) are susceptible to backdoor attacks during
training. The model corrupted in this way functions normally, but when
triggered by certain patterns in the input, produces a predefined target label.
Existing defenses usually rely on the assumption of the universal backdoor
setting in which poisoned samples share the same uniform trigger. However,
recent advanced backdoor attacks show that this assumption is no longer valid
in dynamic backdoors where the triggers vary from input to input, thereby
defeating …

backdoor detection