Photo-response Non-uniformity Estimation Research Articles

PRNU Estimation from Encoded Videos Using Block-Based Weighting

Estimating the photo-response non-uniformity (PRNU) of an imaging sensor from videos is a challenging task due to complications created by several processing steps in the camera imaging pipeline. Among these steps, video coding is one of the most disruptive to PRNU estimation because of its lossy nature. Since videos are always stored in a compressed format, the ability to cope with the disruptive effects of encoding is central to reliable attribution. In this work, by focusing on the block-based operation of widely used video coding standards, we present an improved approach to PRNU estimation that exploits this behavior. To this purpose, several PRNU weighting schemes that utilize block-level parameters, such as encoding block type, quantization strength, and rate-distortion value, are proposed and compared. Our results show that the use of the coding rate of a block serves as a better estimator for the strength of PRNU with almost three times improvement in the matching statistic at low to medium coding bitrates as compared to the basic estimation method developed for photos.

Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos.

Multi-scale segmentation strategies in PRNU-based image tampering localization

With the rapid development of advanced media technology, especially the popularization of digital cameras and image editing software, digital images can be easily forged without leaving visible clues. Therefore, image forensics technology for identifying the accuracy, integrity, and originality of digital images has become increasingly important. Photo-response non-uniformity (PRNU) noise, a unique fingerprint of imaging sensors, is a valuable forgery detection tool because of its consistently good detection performance. All kinds of forgeries, including copy-move and splicing, can be dealt with in a uniform manner. This paper addresses the problem of forgery localization based on PRNU estimation and aims to improve the resolution of PRNU-based algorithms. Different from traditional overlapping and sliding window-based methods, in which PRNU correlations are estimated on overlapped patches, the proposed scheme is analyzed based on nonoverlapping and irregular patches. First, the test image is segmented into nonoverlapped patches with multiple scales. Second, correlations of PRNU are estimated on nonoverlapped patches to obtain the real-valued candidate tampering probability map for each individual scale. Then, all of the candidate maps are fused into a single and more reliable probability map through an adaptive window strategy. In the final step, the final decision map is obtained by adopting a conditional random field (CRF) to model neighborhood interactions. The contributions of this work include the following: a novel PRNU-based forgery localization scheme using multi-scale nonoverlapping segmentation is proposed for the first time. Furthermore, the adaptive fusion strategy involves selecting the best candidate tampering probability individually for each location in the image. Additionally, the experimental results prove that the proposed scheme can achieve much better detection results and robustness compared with the existing state-of-the-art PRNU-based methods.

Robust PRNU estimation from probabilistic raw measurements

Images captured by digital cameras undergo various in-camera processing such as JPEG compression, white balancing, power transforms and other operations to map raw data into nonlinear small gamut image. Due to nonlinear transformation, artifacts or signatures used for camera identification also undergo a significant change. Photo Response Non-Uniformity (PRNU), proved to be useful for uniquely identifying the camera, also undergoes same in-camera operations. Hence estimation of PRNU is affected which often leads to rise in false identification. In this work, we develop a novel algorithm for robust estimation of PRNU from probabilistically obtained raw data. Since not all cameras provide raw data as their output, we compute raw data from the JPEG output using probabilistic color de-rendering procedure. The estimated raw data is modeled as a Poisson process, and Maximum Likelihood Estimation (MLE) is used for PRNU estimation. We then use our estimate of PRNU for identifying the camera using images. We also compare the performance of our algorithm with other state-of-the-art algorithms. Additionally, we demonstrate the robustness of estimate obtained by localizing the forgery in images. The extensive experimental analysis is performed over thousands of patches from various cameras to illustrate the efficiency of proposed approach, which effectively overcomes the state-of-the-art.

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, photo response non_uniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the “Dresden image database.”

Sensor Pattern Noise Estimation Using Probabilistically Estimated RAW Values

Photo response nonuniformity (PRNU) is consider as reliable camera fingerprint for identifying source of a digital images. Digital cameras use various image processing operations to map linear color measurements (raw data) into nonlinear narrow gamut image. This nonlinear transformation affects estimation of PRNU. To undo the effect of nonlinear transformation, in this letter, we propose to estimate PRNU from probabilistically obtained raw values. Since not all cameras provide raw values as their output, we propose to compute estimate of raw values from the JPEG images using probabilistic color derendering procedure. The estimated raw values are modeled as a Poisson process and then maximum likelihood estimation (MLE) is used for PRNU estimation. The experimental results show that, the digital camera identification using our proposed PRNU estimate is better than using other popular PRNU estimate.

A Bayesian-MRF Approach for PRNU-Based Image Forgery Detection

Graphics editing programs of the last generation provide ever more powerful tools, which allow for the retouching of digital images leaving little or no traces of tampering. The reliable detection of image forgeries requires, therefore, a battery of complementary tools that exploit different image properties. Techniques based on the photo-response non-uniformity (PRNU) noise are among the most valuable such tools, since they do not detect the inserted object but rather the absence of the camera PRNU, a sort of camera fingerprint, dealing successfully with forgeries that elude most other detection strategies. In this paper, we propose a new approach to detect image forgeries using sensor pattern noise. Casting the problem in terms of Bayesian estimation, we use a suitable Markov random field prior to model the strong spatial dependences of the source, and take decisions jointly on the whole image rather than individually for each pixel. Modern convex optimization techniques are then adopted to achieve a globally optimal solution and the PRNU estimation is improved by resorting to nonlocal denoising. Large-scale experiments on simulated and real forgeries show that the proposed technique largely improves upon the current state of the art, and that it can be applied with success to a wide range of practical situations.

Improved photo response non-uniformity (PRNU) based source camera identification

The concept of using Photo Response Non-Uniformity (PRNU) as a reliable forensic tool to match an image to a source camera is now well established. Traditionally, the PRNU estimation methodologies have centred on a wavelet based de-noising approach. Resultant filtering artefacts in combination with image and JPEG contamination act to reduce the quality of PRNU estimation. In this paper, it is argued that the application calls for a simplified filtering strategy which at its base level may be realised using a combination of adaptive and median filtering applied in the spatial domain. The proposed filtering method is interlinked with a further two stage enhancement strategy where only pixels in the image having high probabilities of significant PRNU bias are retained. This methodology significantly improves the discrimination between matching and non-matching image data sets over that of the common wavelet filtering approach.