PRNU pattern alignment for images and videos based on scene content

This paper proposes a novel approach for registering the PRNU pattern between different camera acquisition modes by relying on the imaged scene content. First, images are aligned by establishing correspondences between local descriptors: The result can then optionally be refined by maximizing the PRNU correlation. Comparative evaluations show that this approach outperforms those based on brute-force and particle swarm optimization in terms of reliability, accuracy and speed. The proposed scene-based approach for PRNU pattern alignment is suitable for video source identification in multimedia forensics application

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features

Source camera attribution via PRNU emphasis: Towards a generalized multiplicative model

The photoresponse non-uniformity (PRNU) is a camera-specific pattern, which acts as unique fingerprint of any imaging sensor and thus is widely adopted to solve multimedia forensics problems such as device identification or forgery detection. Customarily, the theoretical analysis of this fingerprint relies on a multiplicative model for the denoising residuals. This setup assumes that the nonlinear mapping from scene irradiance to preprocessed luminance, that is, the composition of the Camera Response Function (CRF) with the digital preprocessing pipeline, is a gamma correction. However, this assumption seldom holds in practice. In this paper, we improve the multiplicative model by including the influence of this nonlinear mapping, termed PRNU emphasis, on the denoising residuals. On the theoretical side, we conduct first an exploratory analysis to show that the response of typical cameras deviates from a gamma correction. We also propose a regularized least squares estimator to measure this effect. On the practical side, we argue that the PRNU emphasis is especially beneficial for a source camera attribution problem with cropped images. We back our argument with an extensive empirical evaluation using different denoisers and both compressed and uncompressed images. This new model will pave the way to future PRNU estimators and detectors.Agencia Estatal de Investigación | Ref. PID2019-105717RB-C21Xunta de Galicia | Ref. ED431C 2021/47Universidade de Vigo/CISU

Source Camera Verification from Strongly Stabilized Videos

Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution