Trapdoors in neural networks attract adversarial attacks, making them easier to detect.
problem Adversarial attacks on neural networks are difficult to detect and defend against.
method Intentionally inject trapdoors to attract adversarial attacks, then detect them based on feature similarity.
result Trapdoor-protected models can accurately detect adversarial examples with minimal impact on normal classification.