Color-decoupled photo response non-uniformity for digital image forensics

The last few years have seen the use of photo response non-uniformity noise (PRNU), a unique fingerprint of imaging sensors, in various digital forensic applications such as source device identification, content integrity verification and authentication. However, the use of a colour filter array for capturing only one of the three colour components per pixel introduces colour interpolation noise, while the existing methods for extracting PRNU provide no effective means for addressing this issue. Because the artificial colours obtained through the colour interpolation process is not directly acquired from the scene by physical hardware, we expect that the PRNU extracted from the physical components, which are free from interpolation noise, should be more reliable than that from the artificial channels, which carry interpolation noise. Based on this assumption we propose a Couple-Decoupled PRNU (CD-PRNU) extraction method, which first decomposes each colour channel into 4 sub-images and then extracts the PRNU noise from each sub-image. The PRNU noise patterns of the sub-images are then assembled to get the CD-PRNU. This new method can prevent the interpolation noise from propagating into the physical components, thus improving the accuracy of device identification and image content integrity verification

Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts

In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our schem

Camera model identification based on the generalized noise model in natural images

International audienceThe goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the generalized noise model that is developed by following the image processing pipeline of the digital camera. More specifically, this model is given by starting from the heteroscedastic noise model that describes the linear relation between the expectation and variance of a RAW pixel and taking into account the non-linear effect of gamma correction.The generalized noise model characterizes more accurately a natural image in TIFF or JPEG format. The present paper is similar to our previous work that was proposed for camera model identification from RAW images based on the heteroscedastic noise model. The parameters that are specified in the generalized noise model are used as camera fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its statistical performances are theoretically established. In practice when the model parameters are unknown, two Generalized Likelihood Ratio Tests are designed to deal with this difficulty such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural JPEG images highlight the relevance of the proposed approac

Camera model identification based on DCT coefficient statistics

International audienceThe goal of this paper is to design a statistical test for the camera model identification problem from JPEG images. The approach relies on the camera fingerprint extracted in the Discrete Cosine Transform (DCT) domain based on the state-of-the-art model of DCT coefficients. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its performances are theoretically established. For a practical use, two Generalized Likelihood Ratio Tests are designed to deal with unknown model parameters such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated and real JPEG images highlight the relevance of the proposed approach

RemNet: Remnant Convolutional Neural Network for Camera Model Identification and Image Manipulation Detection

Camera model identification (CMI) and image manipulation detection are of paramount importance in image forensics as digitally altered images are becoming increasingly commonplace. In this thesis, we propose a novel convolutional neural network (CNN) architecture for performing these two crucial tasks. Our proposed Remnant Convolutional Neural Network (RemNet) is designed with emphasis given on the preprocessing task considered to be inevitable for removing the scene content that heavily obscures the camera model fingerprints and image manipulation artifacts. Unlike the conventional approaches where fixed filters are used for preprocessing, the proposed remnant blocks, when coupled with a classification block and trained end-to-end, learn to suppress the unnecessary image contents dynamically. This helps the classification block extract more robust images forensics features from the remnant of the image. We also propose a variant of the network titled L2-constrained Remnant Convolutional Neural Network (L2-constrained RemNet), where an L2 loss is applied to the output of the preprocessor block, and categorical crossentropy loss is calculated based on the output of the classification block. The whole network is trained in an end-to-end manner by minimizing the total loss, which is a combination of the L2 loss and the categorical crossentropy loss. The whole network, consisting of a preprocessing block and a shallow classification block, when trained on 18 models from the Dresden database, shows 100% accuracy for 16 camera models with an overall accuracy of 98.15% on test images from unseen devices and scenes, outperforming the state-of-the-art deep CNNs used in CMI. Furthermore, the proposed remnant blocks, when cascaded with the existing deep CNNs, e.g., ResNet, DenseNet, boost their performances by a large margin. The proposed approach proves to be very robust in identifying the source camera models, even if the original images are post-processed. It also achieves an overall accuracy of 95.49% on the IEEE Signal Processing Cup 2018 dataset, which indicates its generalizability. Furthermore, we attain an overall accuracy of 99.68% in image manipulation detection, which implies that it can be used as a general-purpose network for image forensic tasks

2018 IEEE Signal Processing Cup: Forensic Camera Model Identification Challenge

The goal of this Senior Capstone Project was to lead Union College’s first ever Signal Processing Cup Team to compete in IEEE’s 2018 Signal Processing Cup Competition. This year’s competition was a forensic camera model identification challenge and was divided into two separate stages of competition: Open Competition and Final Competition. Participation in the Open Competition was open to any teams of undergraduate students, but the Final Competition was only open to the three finalists from Open Competition and is scheduled to be held at ICASSP 2018 in Calgary, Alberta, Canada. Teams that make it to the Final Competition will be competing to win a grand prize of $5,000. The goal of this year’s competition required teams to build a classification system that used a combination of various signal processing, machine learning, and image forensic techniques in order to determine the make and model of the camera used to capture a digital image both before and after that image has been post processed. IEEE provided competing teams with an image database consisting of ten different camera models and 275 images accompanying each camera for teams with which to use to train their classification systems. This senior project design report focused on the proposed classification system design that was implemented and submitted on behalf of Union’s Signal Processing Cup Team. The chosen classification system design used methods of re-sampling and re-interpolating in order to build feature spaces based on the relative differences of the original and reconstructed images from the provided image database. These feature spaces were then used to train machine learning classifiers in order to develop an ensemble-based decision fusion to identify camera source. Through the completion of this project, students competing in the IEEE Signal Processing Cup gained experience using signal processing, machine learning, and image forensic techniques to solve challenging information security problems