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In multimedia forensics, learning-based methods provide state-of-the-artperformance in determining origin and authenticity of images and videos.However, most existing methods are challenged by out-of-distribution data,i.e., with characteristics that are not covered in the training set. This makesit difficult to know when to trust a model, particularly for practitioners withlimited technical background.  In this work, we make a first step toward redesigning forensic algorithmswith a strong focus on reliability. To this end, we propose to use Bayesianneural networks (BNN), which combine the power of deep neural networks with therigorous probabilistic formulation of a Bayesian framework. Instead ofproviding a point estimate like standard neural networks, BNNs providedistributions that express both the estimate and also an uncertainty range.  We demonstrate the usefulness of this framework on a classical forensic task:resampling detection. The BNN yields state-of-the-art detection performance,plus excellent capabilities for detecting out-of-distribution samples. This isdemonstrated for three pathologic issues in resampling detection, namely unseenresampling factors, unseen JPEG compression, and unseen resampling algorithms.We hope that this proposal spurs further research toward reliability inmultimedia forensics.

Toward Reliable Models for Authenticating Multimedia Content: Detecting Resampling Artifacts With Bayesian Neural Networks