A Novel Latent-Class Attack and its Detection by Class Subspace Orthogonalization
This work addresses the vulnerability of deep learning models to data poisoning attacks, specifically targeting access control and security systems where misclassification of novel classes could have serious consequences.
The paper introduces a new data poisoning attack called latent class attack, where poisoned examples from an unknown class are mislabeled to a known target class, and proposes a post-training defense using class subspace orthogonalization to detect such attacks without access to the training set.
Deep learning, which in general relies on voluminous amounts of training data, is vulnerable to data poisoning attacks, including error-generic attacks and backdoors (Trojans). In this work, we propose a new data poisoning attack we dub a latent class attack. Here, all poisoned examples are from a class that is novel (unknown) for the given classification domain and are mislabeled to one of the known classes (the target class) of the domain, so that the model learns to recognize the novel class as a sub-class of the target class. Such attacks could be used e.g. to defeat AI-based access control systems, or could cause a "foe" to be classified as a "friend". We also propose a post-training defense to detect this attack, without any access to the training set. This detection approach builds on "class subspace orthogonalization" (CSO), a plug-and-play paradigm demonstrated to improve existing backdoor detectors. Here, CSO is used to seek an input (a putative unknown class instance) whose internal representation is not aligned with any of the known classes, and yet which is classified with confidence to one of these classes. Finally, specific to image classification domains, we propose a method for visualizing the estimated unknown class instance, providing explainability to our latent class detections.