Camera-based Image Forgery Localization using Convolutional Neural Networks

Camera fingerprints are precious tools for a number of image forensics tasks. A well-known example is the photo response non-uniformity (PRNU) noise pattern, a powerful device fingerprint. Here, to address the image forgery localization problem, we rely on noiseprint, a recently proposed CNN-based camera model fingerprint. The CNN is trained to minimize the distance between same-model patches, and maximize the distance otherwise. As a result, the noiseprint accounts for model-related artifacts just like the PRNU accounts for device-related non-uniformities. However, unlike the PRNU, it is only mildly affected by residuals of high-level scene content. The experiments show that the proposed noiseprint-based forgery localization method improves over the PRNU-based reference

Autoencoder with recurrent neural networks for video forgery detection

Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security, and Forensics, January 201

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

Preprocessing reference sensor pattern noise via spectrum equalization

Although sensor pattern noise (SPN) has been proven to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared amongst cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this work, we propose a novel preprocessing approach for attenuating the influence of the nonunique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with 6 SPN extraction or enhancement methods, our proposed Spectrum Equalization Algorithm (SEA) is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. Experimental results indicate that the proposed procedure outperforms, or at least performs comparably to, the existing methods in terms of the overall ROC curve and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks