Photo-response Non-uniformity Noise Research Articles

L-PRNU: Low-Complexity Privacy-Preserving PRNU-Based Camera Attribution Scheme

A personal camera fingerprint can be created from images in social media by using Photo Response Non-Uniformity (PRNU) noise, which is used to identify whether an unknown picture belongs to them. Social media has become ubiquitous in recent years and many of us regularly share photos of our daily lives online. However, due to the ease of creating a PRNU-based camera fingerprint, the privacy leakage problem is taken more seriously. To address this issue, a security scheme based on Boneh–Goh–Nissim (BGN) encryption was proposed in 2021. While effective, the BGN encryption incurs a high run-time computational overhead due to its power computation. Therefore, we devised a new scheme to address this issue, employing polynomial encryption and pixel confusion methods, resulting in a computation time that is over ten times faster than BGN encryption. This eliminates the need to only send critical pixels to a Third-Party Expert in the previous method. Furthermore, our scheme does not require decryption, as polynomial encryption and pixel confusion do not alter the correlation value. Consequently, the scheme we presented surpasses previous methods in both theoretical analysis and experimental performance, being faster and more capable.

Identification of Source Camera by Amalgamation of PRNU and Noise Print Using Dimensionality Expansive Residual Network

It might be challenging in the field of image forensics to identify the source camera of a picture. This researchproposes a noise adaptable convolutional neural network-based technique for camera identification. For cameraidentification, the suggested solution combines Photo Response Non-Uniformity (PRNU) noise and Noiseprint.Three parallel dimensionality expanded residual networks with convolutional layers of kernel size 1x1 were puttogether for enhanced feature extraction. The experiment mentioned above uses pictures from the ”Vision Dataset”as its subject matter. The experimental findings demonstrate the effectiveness of the suggested methodology inidentifying the source camera at the brand, model, and device levels. When two of the three networks were fedwith PRNU and one with noiseprint, the best performance was obtained.

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification.

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

Multimedia source identification using an improved weight photo response non-uniformity noise extraction model in short compressed videos

Source detection of digital images and videos is essential to multimedia security forensics research. Due to the defects of the manufacturing process, the optical sensor generates a photo response non-uniformity noise attached to it when generating multimedia content. This noise is widely regarded as the inherent fingerprint of multimedia due to its uniqueness and can be used for multimedia source detection. However, videos shared on social networking sites will be further compressed. It is difficult for existing methods to extract enough PRNU noise from such compressed videos for source identification, especially short compressed videos. Considering the effect of compression on PRNU noise, a weighted PRNU extraction model based on variance-stabilized transformed multi-scale iterative least-squares filtering is proposed. First, the video’s decoder needs to be reconstructed. When the decoder’s loop filter is removed, more PRNU noise is retained in the video frame. Secondly, a multi-scale iterative least-squares filtering algorithm based on the variance stabilized transform (VST) is proposed, which can extract more PRNU noise from the video frames. Finally, maximum likelihood estimation using quantization parameter (QP) weighting enables a better estimate of the PRNU and suppression of other noise. Experiments on the public VISION database show that the proposed PRNU extraction model improves the recognition performance by about 20% on average over existing models.

Determining image sensor temperature using dark current

ABSTRACT The state of the art method for fingerprinting digital cameras focuses on the non-uniform output of an array of photodiodes due to the distinct construction of the PN junction when excited by photons. This photo-response non-uniformity (PRNU) noise has shown to be effective but ignores knowledge of image sensor output under equilibrium states without excitation (dark current). The dark current response (DSN) traditionally has been deemed unsuitable as a source of fingerprinting as it is unstable across multiple variables including exposure time and temperature. As such it is currently ignored even though studies have shown it to be a viable method similar to that of PRNU. We hypothesize that DSN is not only a viable method for forensic identification but, through proper analysis of the thermal component, can lead to insights regarding the specific temperature at which an individual image under test was taken. We also show that digital filtering based on the discrete cosine transformation, rather than the state-of-the-art wavelet filtering, there is significant computational gain albeit with some performance degradation. This approach is beneficial for triage purposes.

A feature-based approach for digital camera identification using photo-response non-uniformity noise

Source camera identification of an image is an emerging field of digital forensics. To identify the source camera through which the image is captured, photo-response non-uniformity (PRNU) noise is used as a camera fingerprint, as it is a unique characteristic that distinguishes images taken from the similar cameras. This paper presents a feature-based approach to identify the source camera. The input image is denoised using the denoising filter and from this denoised image, PRNU noise pattern is extracted. These PRNU noise patterns are represented by Hu's invariants, which are perpetual under image scaling, translation and rotation. These features are fed to fuzzy min-max neural network (FMNN) for training and classification for digital camera identification. The proposed approach has the ability to identify the cameras capturing the same scene.

A feature-based approach for digital camera identification using photo-response non-uniformity noise

A feature-based approach for digital camera identification using photo-response non-uniformity noise

PRNU Extraction from Stabilized Video: a Patch Maybe Better than a Bunch

This paper presents an algorithm to solve the problem of Photo-Response Non-Uniformity (PRNU) noise facing stabilized video. The stabilized video undergoes in-camera processing like rolling shutter correction. Thus, misalignment exists between the PRNU noises in the adjacent frames owing to the global and local frame registration performed by the in-camera processing. The misalignment makes the reference PRNU noise and the test PRNU noise unable to extract and match accurately. We design a computing method of maximum likelihood estimation algorithm for extracting the PRNU noise from stabilized video frames. Besides, unlike most prior arts tending to match the PRNU noise in whole frame, we propose a new patch-based matching strategy, aiming at reducing the influence from misalignment of frame the PRNU noise. After extracting the reference PRNU noise and the test PRNU noise, this paper adopts the reference and the test PRNU overlapping patch-based matching. It is different from the traditional matching method. This paper conducts different experiments on 224 stabilized videos taken by 13 smartphones in the VISION database. The area under curve of the algorithm proposed in this paper is 0.841, which is significantly higher than 0.805 of the whole frame matching in the traditional algorithm. Experimental results show good performance and effectiveness the proposed strategy by comparing with the prior arts.

A fast source camera identification and verification method based on PRNU analysis for use in video forensic investigations

Due to the rapid development of digital and cloud technologies, everyone can easily shoot and spread digital videos via email or social media. However, it is difficult for law enforcement to trace the origin of those digital videos, while some videos or images containing illegal information such as personal privacy, obscene pornography, and national security-related content. Recently, a significant breakthrough is achieved by using Photo-Response Non-Uniformity (PRNU) noise to characterize the camera sensor. However, PRNU analysis is often carried out on a frame-by-frame basis. As a result, the processing time is unbearable when treating a large set of videos and devices. In this paper, we propose a novel video forensic method considering both cameras rolling and I-frame of videos to improve the processing time and accuracy. Experimental results demonstrate that our proposed method is at a minimum of 15 times on average faster than the most wildly used method, PRNU analysis, and reduce the false positive rate as compared to existing methods used in the field of the forensic examination.

A Leak in PRNU Based Source Identification—Questioning Fingerprint Uniqueness

Photo Response Non-Uniformity (PRNU) is considered the most effective trace for the image source attribution task. Its uniqueness ensures that the sensor pattern noises extracted from different cameras are strongly uncorrelated, even when they belong to the same camera model. However, with the advent of computational photography, most recent devices heavily process the acquired pixels, possibly introducing non-unique artifacts that may reduce PRNU noise's distinctiveness, especially when several exemplars of the same device model are involved in the analysis. Considering that PRNU is an image forensic technology that finds actual and wide use by law enforcement agencies worldwide, it is essential to keep validating such technology on recent devices as they appear. In this paper, we perform an extensive testing campaign on over 33.000 Flickr images belonging to 45 smartphone and 25 DSLR camera models released recently to determine how widespread the issue is and which is the plausible cause. Experiments highlight that most brands, like Samsung, Huawei, Canon, Nikon, Fujifilm, Sigma, and Leica, are strongly affected by this issue. We show that the primary cause of high false alarm rates cannot be directly related to specific camera models, firmware, nor image contents. It is evident that the effectiveness of \prnu based source identification on the most recent devices must be reconsidered in light of these results. Therefore, this paper is intended as a call to action for the scientific community rather than a complete treatment of the subject. Moreover, we believe publishing these data is important to raise awareness about a possible issue with PRNU reliability in the law enforcement world.

The PRNU Algorithm of Video Forensics

The A video sequence provides more proof of an occurring event than a photo or a group of photos. Hence it is important to identify the source of the video. In this paper, we explore how the Photo Response Non-Uniformity (PRNU) noise is useful for the detection of source cameras- Metadata of a Camera. With this method, we aim to determine whether the video originated from the given camera or not.

PRNU-Based Content Forgery Localization Augmented With Image Segmentation

The advances in image editing and retouching technology have enabled an unskilled person to easily produce visually indistinguishable forged images. To detect and localize such forgeries, many image forensic tools rely on visually imperceptible clues, e.g. the subtle traces or artifacts introduced during image acquisition and processing, and slide a regular, typically square, detection window across the image to search for discrepancies of specific clues. Such a sliding-window paradigm confines the explorable neighborhood to a regular grid and inevitably limits its capability in localizing forgeries in a broad range of shapes. While image segmentation that generates segments adhering to object boundaries might be a promising alternative to the sliding window-based approach, it is generally believed that the potential of the segmentation-based detection scheme is hindered by object removal forgeries where meaningful segments are often unavailable. In this work, we take forgery localization based on photo-response non-uniformity (PRNU) noise as an example and propose a segmentation-based forgery localization scheme that exploits the local homogeneity of visually indiscernible clues to mitigate the limitations of existing segmentation approaches that are merely based on visually perceptible content. We further propose a multi-orientation localization scheme that integrates the forgery probabilities obtained with image segmentation and multi-orientated detection windows. The multi-orientation scheme aggregates the complementary strengths of image segmentation and multi-oriented detection window in localizing the object insert and object removal forgeries. Experimental results on a public realistic tampering image dataset demonstrate that the proposed segmentation-based and multi-orientation forgery localization schemes outperform existing state-of-the-art PRNU-based forgery localizers in terms of both region and boundary $F_{1}$ scores.

PRNU based source camera attribution for image sets anonymized with patch-match algorithm

Patch-Match is an efficient algorithm used for structural image editing and available as a tool on popular commercial photo-editing software. The tool allows users to insert or remove objects from photos using information from similar scene content. Recently, a modified version of this algorithm was proposed as a counter-measure against Photo-Response Non-Uniformity (PRNU) based Source Camera Identification (SCI). The algorithm can provide anonymity at a great rate (97%) and impede PRNU based SCI without the need of any other information, hence leaving no-known recourse for the PRNU-based SCI. In this paper, we propose a method to identify sources of the Patch-Match-applied images by using randomized subsets of images and the traditional PRNU based SCI methods. We evaluate the proposed method on two forensics scenarios in which an adversary makes use of the Patch-Match algorithm and distorts the PRNU noise pattern in the incriminating images she took with his camera. Our results show that it is possible to link sets of Patch-Match-applied images back to their source camera even in the presence of images that come from unknown cameras. To our best knowledge, the proposed method represents the very first counter-measure against the usage of Patch-Match in the digital forensics literature.

Extended noise equalisation for image compression in microscopical applications

Abstract Today’s camera systems used for machine vision and scientific applications have intra-scene dynamic ranges up to 16 bit and therefore A/D converters with up to 16 bit resolution per pixel. Unfortunately, the linear amplification of electrons also forces a linear or even quadratic increase of the image noise variance with the signal. Based on a method published in 2016 (B. Jähne, M. Schwarzbauer, tm-Technisches Messen 83.1), this paper describes a more general nonlinear transformation which equalizes the combined effect of temporal noise and photo-response non-uniformity (PRNU) and/or temporal noise in the illumination system of an image sensor. With this generalisation it is possible to use the equalisation also for microscopic applications for which an example is discussed.

PRNU-based source device attribution for YouTube videos

Photo Response Non-Uniformity (PRNU) is a camera imaging sensor imperfection which has earned a great interest for source device attribution of digital videos. A majority of recent researches about PRNU-based source device attribution for digital videos do not take into consideration the effects of video compression on the PRNU noise in video frames, but rather consider video frames as isolated images of equal importance. As a result, these methods perform poorly on re-compressed or low bit-rate videos. This paper proposes a novel method for PRNU fingerprint estimation from video frames taking into account the effects of video compression on the PRNU noise in these frames. With this method, we aim to determine whether two videos from unknown sources originate from the same device or not. Experimental results on a large set of videos show that the method we propose is more effective than existing frame-based methods that use either only I frames or all (I-B-P) frames, especially on YouTube videos.

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.

An analysis of optical contributions to a photo-sensor's ballistic fingerprints

Lens aberrations have previously been used to determine the provenance of an image. However, this is not necessarily unique to an image sensor, as lens systems are often interchanged. Photo-response non-uniformity noise was proposed in 2005 by Lukáš, Goljan and Fridrich as a stochastic signal which describes a sensor uniquely, akin to a “ballistic” fingerprint. This method, however, did not account for additional sources of bias such as lens artefacts and temperature.In this paper, we propose a new additive signal model to account for artefacts previously thought to have been isolated from the ballistic fingerprint. Our proposed model separates sensor level artefacts from the lens optical system and thus accounts for lens aberrations previously thought to be filtered out. Specifically, we apply standard image processing theory, an understanding of frequency properties relating to the physics of light and temperature response of sensor dark current to classify artefacts. This model enables us to isolate and account for bias from the lens optical system and temperature within the current model.

Isolating lens effects from source camera identification using sensor pattern noise

ABSTRACTLens aberrations can be used to determine the provenance of camera optics in an image. However, this method does not necessarily identify the camera itself, since lens systems are often interchanged. A method, first published by Lukàš, Fridrich and Goljan, proposed using Fixed Pattern Noise to link a photo to an image sensor using sensor pattern noise. However, their model did not fully account for lens artefacts or temperature. In our work, we have applied standard image processing theory and an understanding of the geometric properties of light and sensor dark current to continue the isolation of artefacts within the sensor pattern noise model. We use three image sensors to take images using six integrated lenses and prepare reference patterns based on the lens for each camera. We then repeat our experiments with a pinhole lens of our design before comparing the correlation energy contained within each. We show that majority of the unique signal power used to correlate an image to a specific camera is related to the photo response non-uniformity noise; however, additional signal power can also be attributed to the optical system of the camera as well as the sensor’s dark current.

A new method estimating linear gaussian filter kernel by image PRNU noise

In the paper, we present a method estimating the linear Gaussian filter kernel by means of photo-response non-uniformity (PRNU) noise. In the past decade, a large number of works have proved that PRNU noise is a reliable fingerprint for digital image. Accordingly, PRNU noise has been widely used in such fields as camera identification and image manipulation detection. Specifically, in order to detect whether the query image is a Gaussian filtered image and estimate the filter kernel of the Gaussian filtered image, the proposed paper takes fully the camera reference PRNU noise as the identification fingerprint, and we correlate the identification fingerprint with the noise residual extracted from clean image and noise residual extracted from filtered image, respectively. Mathematically, the linear correlation between the two cross-correlation can be determined. In addition, due to the unknown of clean image, how to access to a clean image without additive noise is crucial in this application. Consequently, we propose to use the method of recapturing the query image instead of randomly shooting a large number of content-irrelevant images. It exhibits better estimation result compared to the existing techniques. Moreover, we observe that the proposed method is robust to JPEG compression.

Camera recognition with deep learning

In this paper, camera recognition with the use of deep learning technique is introduced. To identify the various cameras, their characteristic photo-response non-uniformity (PRNU) noise pattern was extracted. In forensic science, it is important, especially for child pornography cases, to link a photo or a set of photos to a specific camera. Deep learning is a sub-field of machine learning which trains the computer as a human brain to recognize similarities and differences by scanning it, in order to identify an object. The innovation of this research is the use of PRNU noise patterns and a deep learning technique in order to achieve camera identification. In this paper, AlexNet was modified producing an improved training procedure with high maximum accuracy of 80%–90%. DIGITS showed to have identified correctly six cameras out of 10 with a success rate higher than 75% in the database. However, many of the cameras were falsely identified indicating a fault occurring during the procedure. A possible explanation for this is that the PRNU signal is based on the quality of the sensor and the artefacts introduced during the production process of the camera. Some manufacturers may use the same or similar imaging sensors, which could result in similar PRNU noise patterns. In an attempt to form a database which contained different cameras of the same model as different categories, the accuracy rate was low. This provided further proof of the limitations of this technique, since PRNU is stochastic in nature and should be able to distinguish between different cameras from the same brand. Therefore, this study showed that current convolutional neural networks (CNNs) cannot achieve individualization with PRNU patterns. Nevertheless, the paper provided material for further research.