Forensic analysis of AI-compression traces in spatial and frequency domain - Bergmann, Sandra; Moussa, Denise; Brand, Fabian; Kaup, André; Rieß, Christian (Pattern Recognition Letters, 2024)
The classical JPEG compression is a rich source of cues for forensic image analysis. However, this compression standard will in the near future be complemented by a new, highly efficient learning-based compression standard called JPEG AI. JPEG AI is fundamentally different from classical JPEG. Hence, its forensic traces can also be expected to be fundamentally different. We argue that there is a pressing need for image forensics research to investigate these traces. In this work, we characterize forensic compression traces of different AI compression algorithms. Our analysis investigates AI compression artifacts in frequency domain and in spatial domain. Both domains exhibit similar artifacts that likely stem from upsampling operations of the decoders. Additionally, we report for one AI codec another artifact in homogeneous regions. We also investigate the artifact detectability in several scenarios including unseen AI compression traces and postprocessing. Here, frequency and autocorrelation features are better on additive noise and classical JPEG post-compression, while RGB features perform better on blurred and downsampled images.

Frequency-Domain Analysis of Traces for the Detection of AI-based Compression - Bergmann, Sandra; Moussa, Denise; Brand, Fabian; Kaup, André; Rieß, Christian (2023 International Workshop on Biometrics and Forensics (IWBF), 2023)
The JPEG algorithm is the most popular compression method on the internet. Its properties have been extensively studied in image forensics for examining image origin and authenticity. However, the JPEG standard will in the near future be extended with AI-based compression. This approach is fundamentally different from the classic JPEG algorithm, and requires an entirely new set of forensics tools. As a first step towards forensic tools for AI compression, we present a first investigation of forensic traces in HiFiC, the current state-of-the-art AI-based compression method. We investigate the frequency space of the compressed images, and identify two types of traces, which likely arise from GAN upsampling and in homogeneous areas. We evaluate the detectability on different patch sizes and unseen postprocessing, and report a detectability of 96.37%. Our empirical results also suggest that further, yet unidentified, compression traces can be expected in the spatial domain.<br
Keywords: AI-based compression; frequency analysis

Unmasking Neural Codecs: Forensic Identification of AI-compressed Speech - Moussa, Denise; Bergmann, Sandra; Rieß, Christian (Proc. Interspeech 2024, 2024)
Compression traces are an important forensic cue to uncover the processing history and integrity of audio evidence. With continuous advances in the AI domain, efficient generative lossy neural codecs like Lyra-V2, EnCodec or Improved RVQGAN can compete with traditional speech and audio codecs. Their fundamentally different learning based approach compared to analytical lossy compression methods poses a new challenge for audio forensics. This calls for a closer examination of such techniques to prepare forensics for audio evidence processed by AI-based codecs. In this work, we thus want to take a first step towards robustly detecting traces of neural codecs in audio samples. We report that distinctive frequency artefacts enable for identifying neurally compressed audio and fingerprint specific AI-based codecs. We further analyse the robustness towards cross-dataset testing and noise, downsampling, and traditional compression post-processin
Keywords: AI Audio Compression; Audio Forensics