Alexander Schlögl

Alexander Schlögl, Tobias Kupek, Rainer Böhme

We propose methods to infer properties of the execution environment of machine learning pipelines by tracing characteristic numerical deviations in observable outputs. Results from a series of proof-of-concept experiments obtained on local and cloud-hosted machines give rise to possible forensic applications, such as the identification of the hardware platform used to produce deep neural network predictions. Finally, we introduce boundary samples that amplify the numerical deviations in order to distinguish machines by their predicted label only.

Pascal Schöttle, Alexander Schlögl, Cecilia Pasquini et al.

Adversarial classification is the task of performing robust classification in the presence of a strategic attacker. Originating from information hiding and multimedia forensics, adversarial classification recently received a lot of attention in a broader security context. In the domain of machine learning-based image classification, adversarial classification can be interpreted as detecting so-called adversarial examples, which are slightly altered versions of benign images. They are specifically crafted to be misclassified with a very high probability by the classifier under attack. Neural networks, which dominate among modern image classifiers, have been shown to be especially vulnerable to these adversarial examples. However, detecting subtle changes in digital images has always been the goal of multimedia forensics and steganalysis. In this paper, we highlight the parallels between these two fields and secure machine learning. Furthermore, we adapt a linear filter, similar to early steganalysis methods, to detect adversarial examples that are generated with the projected gradient descent (PGD) method, the state-of-the-art algorithm for this task. We test our method on the MNIST database and show for several parameter combinations of PGD that our method can reliably detect adversarial examples. Additionally, the combination of adversarial re-training and our detection method effectively reduces the attack surface of attacks against neural networks. Thus, we conclude that adversarial examples for image classification possibly do not withstand detection methods from steganalysis, and future work should explore the effectiveness of known techniques from multimedia forensics in other adversarial settings.