We present DeClaW, a system for detecting, classifying, and warning of
adversarial inputs presented to a classification neural network. In contrast to
current state-of-the-art methods that, given an input, detect whether an input
is clean or adversarial, we aim to also identify the types of adversarial
attack (e.g., PGD, Carlini-Wagner or clean). To achieve this, we extract
statistical profiles, which we term as anomaly feature vectors, from a set of
latent features. Preliminary findings suggest that AFVs can help distinguish
among several types of adversarial attacks (e.g., PGD versus Carlini-Wagner)
with close to 93% accuracy on the CIFAR-10 dataset. The results open the door
to using AFV-based methods for exploring not only adversarial attack detection
but also classification of the attack type and then design of attack-specific
mitigation strategies.

DeClaW 是一个可以检测、分类和警告分类神经网络中对抗性输入的系统，它可以从潜在特征中提取异常特征向量，以准确识别对抗攻击类型（例如 PGD、Carlini-Wagner 或清洁），初步发现表明该方法可以在 CIFAR-10 数据集上实现近 93% 的准确性。这些结果不仅可以用于探索对抗性攻击检测，还可以用于攻击类型分类和攻击特定的缓解策略设计。