Detectability-Based JPEG Steganography Modeling the Processing Pipeline: The Noise-Content Trade-off (2:20 PM – 2:40 PM) Quentin Giboulot, Rémi Cogranne and Patrick Bas – On-site presentation IEEE Transactions on Information Forensics and Security 2021, vol. 16, DOI: 10.1109/TIFS.2021.3050063
The current art of steganography shows that schemes using a deflection criterion (such as MiPOD) for JPEG steganography are usually subpar with respect to distortion-based schemes. We link this lack of performance to a poor estimation of the variance of the model of the noise on the cover image. However, this statistically-based method provides a better assessment of the detectability of hidden data as well as theoretical guarantees under a given model. In this paper, we propose a method to obtain better estimates of the variances of DCT coefficients by taking into account the dependencies introduced by development pipeline on pixels. A second method, which is a side-informed extension of Gaussian Embedding in the JPEG domain using quantization error as side-information, is also formulated and shown to achieve state-of-the-art performances. Eventually, the trade-off between noise and content complexity in steganography is thoroughly analyzed through the lenses of these two new methods using a wide range of numerical experiments.
Non-Degraded Adaptive HEVC Steganography by Advanced Motion Vector Prediction (2:40 PM – 3:00 PM)
Shuowei Liu, Beibei Liu, Yongjian Hu and Xianfeng Zhao – Virtual presentation IEEE Signal Processing Letters 2021, vol. 28, DOI: 10.1109/LSP.2021.3111565
Current video steganography operates with either the decoded frame images or the compression coding parameters, which could cause quality degradation of the reconstructed frames. In this letter, by exploiting the advanced motion vector prediction (AMVP) technique of High Efficiency Video Coding (HEVC) standard, we propose a non-degraded adaptive steganographic approach for H.265/HEVC videos. The index value in the candidate list of the prediction unit (PU) is used for embedding. Experimental results demonstrate the superiority of the proposed steganographic approach against both hand-crafted feature-based and deep learning network-based steganalytic detectors. Our work explores a new embedding space that is not previously studied. It is a significant development in finding new ways to escape from video quality change-based steganalysis.
Reliable Camera Model Identification Using Sparse Gaussian Processes (3:00 PM – 3:20 PM) Benedikt Lorch, Franziska Schirrmacher, Anatol Maier and Christian Riess – On-site presentation IEEE Signal Processing Letters 2021, vol. 28, DOI: 10.1109/LSP.2021.3070206
Identifying the model of a camera that has captured an image can be an important task in criminal investigations. Many methods assume that the image under analysis originates from a given set of known camera models. In practice, however, a photo can come from an unknown camera model, or its appearance could have been altered by unknown post-processing. In such a case, forensic detectors are prone to fail silently. One way to mitigate silent failures is to use a rejection mechanism for unknown examples. In this work, we propose Gaussian processes (GPs), which intrinsically provide such a rejection mechanism. This makes GPs a potentially powerful tool in multimedia forensics, where forensic analysts regularly work on images from unknown origins. We demonstrate that GPs scale well to the task of camera model identification. Probabilistic predictions from a GP classifier achieve high classification accuracy for known camera models while providing reliable uncertainty estimates. The built-in uncertainty estimates effectively tackle open-set camera model identification, outperforming two state-of-the-art methods.
