Recently, deep learning techniques have gained popularity in multimedia forensics research designed to accomplish tasks such as camera model identification. However, despite the success of deep learning techniques, research has shown that they are vulnerable to adversarial perturbations. These adversarial perturbations can cause deep learning classifiers to misclassify images even though the perturbations are imperceptible to human eyes. To understand the vulnerabilities of deep-learning-based forensic algorithms, we propose a novel anti-forensic framework inspired by generative adversarial networks that is capable of falsifying an image’s source camera model. To accomplish this, we design a generator to anti-forensically falsify camera model traces in an image without introducing visually perceptible changes or artifacts. We propose two techniques to adversarially train this generator depending on the knowledge available to the attacker. In a white-box scenario when complete knowledge of an investigator’s camera model identification network is available to an attacker, we directly incorporate the network into our generator’s adversarial training strategy. In a black-box scenario when no internal details of the camera model classifier are available to the attacker, we construct a substitute network to mimic its decisions, then utilize this substitute network to adversarially train our generator. We conduct a series of experiments to evaluate the performance of our attack against several well-known CNNbased camera model classifiers. Experimental results show that our attack can successfully fool these CNNs in both white-box and black-box scenarios. Furthermore, our attack maintains high image quality and can be generalized to attack images from arbitrary source camera models.

While significant work has been conducted to perform source camera model identification for images, little work has been done specifically for video camera model identification. This is problematic because different forensic traces may be left in digital images and videos captured by the same camera. As our experiments in this paper will show, a system trained to perform camera model identification for images yields unacceptably low performance when given video frames from the same cameras. To overcome this problem, new systems for identifying a videos source must be developed. In this paper, we propose a deep learning based system for determining the source camera model that captured a digital video. To do this, we use a convolutional neural network to produce camera model identification scores for small patches taken from video frames. These patches are chosen by a patch selection system that obtains patches from several appropriate frames temporally distributed throughout the video. Forensic information obtained by the CNN is provided to a fusion system, which combines it to produce a single, more accurate identification result. Through a series of experiments, we evaluate several system design choices and show that our system can achieve 95.9% video camera model identification accuracy.

Deep learning techniques have become popular for performing camera model identification. To expose weaknesses in these methods, we propose a new anti-forensic framework that utilizes a generative adversarial network (GAN) to falsify an image's source camera model. Our proposed attack uses the generator trained in the GAN to produce an image that can fool a CNN-based camera model identification classifier. Moreover, our proposed attack will only introduce a minimal amount of distortion to the falsified image that is not perceptible to human eyes. By conducting experiments on a large amount of data, we show that the proposed attack can successfully fool a state-of-art camera model identification CNN classifier with 98% probability and maintain high image quality.

Image filters have become a popular feature of photo editing software and camera phones. Filter identification can provide useful information for us to determine source and processing history of images. Currently, there is no forensic work done to perform filter identification. In this paper, we propose a framework to search for color correlations left by different filters in a set of interpolation residuals obtained from various demosaicing algorithms. To effectively capture the structures of color correlations, we design a diverse set of geometric co-occurrence patterns and gather both intra-channel and inter-channel color dependencies using co-occurrence matrices. Experiments conducted on two large image databases full demonstrate the ability of our framework to identify a wide range of filters provided by both cameras and third-party software.

Many forensic algorithms have been developed to determine the model of an image's source camera by examining traces left by the camera's demosaicing algorithm. An anti-forensic attacker, however, can falsify these traces by maliciously using existing forensic techniques to estimate one camera's demosaicing filter, then use these estimates to re-demosaic an image captured by a different camera. Currently, there is no known defense against this attack, which is capable of fooling existing camera model identification algorithms. In this paper, we propose a new method to detect if an image's source camera model has been anti-forensically falsified. Our algorithm operates by characterizing the different content-independent local pixel relationships that are introduced by both authentic demosaicing algorithms and anti-forensic attacks. Experimental results show that our algorithm can detect an anti-forensic attack with over 99% accuracy, is robust to JPEG compression, and can even identify the true source camera model in certain circumstances.