# The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices Source: [The "Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:53:04.613Z Authors: Wanlun Ma, Derui Wang, Ruoxi Sun, Minhui Xue, Sheng Wen, Yang Xiang Published: 2022-09-23T16:47:19.000Z ## Abstract 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 However, recent advanced backdoor attacks show that this assumption is no longer valid in dynamic backdoors where the triggers vary from input to input, thereby defeating the existing defenses. In this work, we propose a novel technique, Beatrix (backdoor detection via Gram matrix). Beatrix utilizes Gram matrix to capture not only the feature correlations but also the appropriately high-order information of the representations. By learning class-conditional statistics from activation patterns of normal samples, Beatrix can identify poisoned samples by capturing the anomalies in activation patterns. To further improve the performance in identifying target labels, Beatrix leverages kernel-based testing without making any prior assumptions on representation distribution. We demonstrate the effectiveness of our method through extensive evaluation and comparison with state-of-the-art defensive techniques. The experimental results show that our approach achieves an F1 score of 91.1% in detecting dynamic backdoors, while the state of the art can only reach 36.9%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Adversarial Defense on ImageNet-1K 1.0 (test)](): Adversarial Defense; ImageNet-1k 1.0 (test); Classification Accuracy: not recorded. - [Backdoor Defense (Multimodal) on CC3M 60K subset 1.0 (test)](): Backdoor Defense (Multimodal); CC3M 60K subset 1.0 (test); Clean Accuracy (CA): not recorded. - [Unimodal Backdoor Defense on ImageNet-1K 1.0 (test)](): Backdoor Defense (Unimodal); ImageNet-1k 1.0 (test); Clean Accuracy (CA): not recorded. ## Original sources and implementation [Original paper]() [PDF]()