Source Camera Research Articles

Hierarchical deep learning approach using fusion layer for Source Camera Model Identification based on video taken by smartphone

Over the last decade, videos uploaded and shared through web-based multimedia platforms and mobile applications have proliferated worldwide. This is because cloud-based applications such as iCloud, YouTube, Facebook, Twitter, and WhatsApp offer affordable and secure environments for video storage and sharing. However, new challenges have emerged alarming forensic analysts and investigators since videos can be used to commit heinous crimes such as blackmail, fraud, and forgery. Source Camera Identification (SCI) has become of paramount importance in the field of image and video forensics. Camera model identification can also help identify the perpetrators or narrow down the search and can be used to enhance SCI systems. In this context, existing approaches such as the Photo Response Non-Uniformity (PRNU) based methods and machine learning techniques such as the support vector machine (SVM) and deep learning models are commonly used solutions. This work exploits these two categories of methods by exploring a hierarchical deep learning model for camera model identification based on smartphone videos. The PRNU features are extracted by CNN-based structures during the training process. Proposed six-stream networks are leveraged to extract both low-level and high-level features through the network. A fusion layer is created based on joint sparse representation using forward and backward functions defined for fusing the proposed six streams. The proposed approach has been implemented and evaluated through intensive experiments, and results showed successful camera model identification with a performance at the frame level reaching an average accuracy of 69.9% for the Daxing dataset and 81.6% for the QUFVD dataset.

Source Camera Identification with a Robust Device Fingerprint: Evolution from Image-Based to Video-Based Approaches.

With the increasing prevalence of digital multimedia content, the need for reliable and accurate source camera identification has become crucial in applications such as digital forensics. While effective techniques exist for identifying the source camera of images, video-based source identification presents unique challenges due to disruptive effects introduced during video processing, such as compression artifacts and pixel misalignment caused by techniques like video coding and stabilization. These effects render existing approaches, which rely on high-frequency camera fingerprints like Photo Response Non-Uniformity (PRNU), inadequate for video-based identification. To address this challenge, we propose a novel approach that builds upon the image-based source identification technique. Leveraging a global stochastic fingerprint residing in the low- and mid-frequency bands, we exploit its resilience to disruptive effects in the high-frequency bands, envisioning its potential for video-based source identification. Through comprehensive evaluation on recent smartphones dataset, we establish new benchmarks for source camera model and individual device identification, surpassing state-of-the-art techniques. While conventional image-based methods struggle in video contexts, our approach unifies image and video source identification through a single framework powered by the novel non-PRNU device-specific fingerprint. This contribution expands the existing body of knowledge in the field of multimedia forensics.

CAMID: An assuasive approach to reveal source camera through inconspicuous evidence

Identifying the source camera of digital images is an important problem due to the abundance of such images nowadays. Although photo response non-uniformities (PRNUs) can accurately identify the source camera, seam-carving techniques have been introduced to hide the cameras’ identity by invalidating the positional correspondence assumption in the PRNU. Especially, the seam carving techniques create irregular carving patterns, which makes source camera identification from seam-carved images challenging. In this paper, we propose a feature-based method called CAM1D, to identify the source camera of seam-carved images. CAM1D constructs a series of camera signatures to enhance robustness and reliability, rather than relying on a single PRNU for identification. Useful features are extracted from the correlation between a series of camera signatures and seam-carved photos, giving high source camera identification accuracy. Experimental results using the confusion matrix and F1 score confirm the effectiveness of CAM1D.

TrackAER: real-time event-based quantitative flow visualization

We present a novel event-based quantitative flow visualization system, TrackAER, capable of continuously reconstructing, rendering and recording particle tracks in large test volumes without limitations on the measurement duration. Multiple event-based cameras are synchronized and calibrated to produce independent and asynchronous, yet temporally co-registered data streams of flow tracer positions. Subsequently, these data streams are merged into time-resolved three-dimensional particle tracks using photogrammetric techniques. Due to the operating principle of event cameras, the flow scenery is reduced to moving objects only, which effectively compresses the data stream at the camera source. In combination with an efficient data processing pipeline, the measurement system operates in real-time, reconstructing and rendering the investigated flow field without noticeable time lag. The data processing approach follows a “per-event” paradigm and enables the immediate observation and analysis of both, transient and long duration flow features. Specific issues resulting from event-based, frame-free processing are discussed as well as the advantages and limitations of event cameras. Exemplary results are provided to demonstrate the utility of the TrackAER system where Lagrangian particle track information is displayed in a virtual scene together with extracted quantitative information such as local flow velocities.

Source camera identification based on an adaptive dual-branch fusion residual network

Source camera identification based on an adaptive dual-branch fusion residual network

IMGCAT: An approach to dismantle the anonymity of a source camera using correlative features and an integrated 1D convolutional neural network

With the proliferation of smartphones, digital data collection has become trivial. The ability to analyze images has increased, but source authentication has stagnated. Editing and tampering of images has become more common with advancements in signal processing technology. Recent developments have introduced the use of seam carving (insertion and deletion) techniques to disguise the identity of the camera, specifically in the child pornography market. In this article, we focus on the available features in the image based on PRNU (photo response nonuniformity). The forced-seam sculpting technique is a well-known method to create occlusion for camera attribution by injecting seams into each 50 × 50 pixel block. To counter this, we perform camera identification using a 1D CNN integrated with feature extractions on 20 × 20 pixel blocks. We achieve state-of-the-art performance for our proposed IMGCAT (image categorization) in three-class classification over the baselines (original, seam removed, seam inserted). Based on our experimental findings, our model is robust when dealing with blind facts related to the questionable camera.

Radon transform of image monotonic rearrangements as feature for noise sensor signature

Source camera identification represents a delicate, crucial but challenging task in digital forensics, especially when an image has to be used as a proof in a court of law. This paper investigates some properties of the Photo Response Non Uniformity (PRNU) pattern noise that represents the fingerprint of any acquisition sensor. The main goal is to define specific and distinctive features for this noise source that enable the identification of the acquisition sensor by simply analysing a few images. These features are required to be independent of image size, modifications, storage mode, etc. The discrimination power of the decreasing rearrangement of a function, combined with the Radon transform, has been investigated in this paper. Preliminary tests show that a proper rearrangement of PRNU image provides specific and device-dependent geometric structures that can be properly coded through the Radon transform. In particular, the empirical distribution of the Radon Transform of rearranged Flat Field images alone is capable to correctly characterize each device with high accuracy, showing robusteness to some standard image modifications, such as quantization and blurring; in addition, it guarantees independence of image size.

A Novel PCA-Based Method for PRNU Distillation to the Benefit of Source Camera Identification

Photo response non-uniformity (PRNU) is a widely accepted inherent fingerprint that has been used in source camera identification (SCI). However, the reference PRNU noise is limited by the performance of PRNU noise extraction techniques and is easily contaminated by interfering noise from image content. The existing methods mainly suppressed the interference noise of the reference PRNU noise in the spectral domain, but there was still interference noise related to the image content in the low-frequency region. We considered that this interference noise of the reference PRNU noise could be removed by further operations in the spatial domain. In this paper, we proposed a scheme to distil the reference PRNU by removing the interference noise with the help of principal component analysis (PCA) technology. Specifically, the reference PRNU noise was modelled as white Gaussian noise, whereas the interfering noise caused correlation between pixels and their neighbourhoods in the reference PRNU noise. In the local pixel area, we modelled a pixel and its neighbours as a vector and used block matching to select PCA training samples with similar contents. Next, PCA transformation estimated the interference noise in the local pixel area, and we performed coefficient shrinkage in the PCA domain to better estimate interference noise. The experimental results on the “Dresden” and “VISION” datasets showed that the proposed scheme achieved better receiver operating characteristic curves and the Kappa statistic than state-of-the-art works.

Neural noiseprint transfer: a generic noiseprint-based counter forensics framework.

A noiseprint is a camera-related artifact that can be extracted from an image to serve as a powerful tool for several forensic tasks. The noiseprint is built with a deep learning data-driven approach that is trained to produce unique noise residuals with clear traces of camera-related artifacts. This data-driven approach results in a complex relationship that governs the noiseprint with the input image, making it challenging to attack. This article proposes a novel neural noiseprint transfer framework for noiseprint-based counter forensics. Given an authentic image and a forged image, the proposed framework synthesizes a newly generated image that is visually imperceptible to the forged image, but its noiseprint is very close to the noiseprint of the authentic one, to make it appear as if it is authentic and thus renders the noiseprint-based forensics ineffective. Based on deep content and noiseprint representations of the forged and authentic images, we implement the proposed framework in two different approaches. The first is an optimization-based approach that synthesizes the generated image by minimizing the difference between its content representation with the content representation of the forged image while, at the same time, minimizing the noiseprint representation difference from the authentic one. The second approach is a noiseprint injection-based approach, which first trains a novel neural noiseprint-injector network that can inject the noiseprint of an image into another one. Then, the trained noiseprint-injector is used to inject the noiseprint from the authentic image into the forged one to produce the generated image. The proposed approaches are generic and do not require training for specific images or camera models. Both approaches are evaluated on several datasets against two common forensic tasks: the forgery localization and camera source identification tasks. In the two tasks, the proposed approaches are able to significantly reduce several forensic accuracy scores compared with two noiseprint-based forensics methods while at the same time producing high-fidelity images. On the DSO-1 dataset, the reduction in the forensic accuracy scores has an average of 75%, while the produced images have an average PSNR of 31.5 dB and SSIM of 0.9. The source code of the proposed approaches is available on GitHub (https://github.com/ahmed-elliethy/nnt).

Exploiting the Rolling Shutter Read-Out Time for ENF-Based Camera Identification

The electric network frequency (ENF) is a signal that varies over time and represents the frequency of the energy supplied by a mains power system. It continually varies around a nominal value of 50/60 Hz as a result of fluctuations over time in the supply and demand of power and has been employed for various forensic applications. Based on these ENF fluctuations, the intensity of illumination of a light source powered by the electrical grid similarly fluctuates. Videos recorded under such light sources may capture the ENF and hence can be analyzed to extract the ENF. Cameras using the rolling shutter sampling mechanism acquire each row of a video frame sequentially at a time, referred to as the read-out time (Tro) which is a camera-specific parameter. This parameter can be exploited for camera forensic applications. In this paper, we present an approach that exploits the ENF and the Tro to identify the source camera of an ENF-containing video of unknown source. The suggested approach considers a practical scenario where a video obtained from the public, including social media, is investigated by law enforcement to ascertain if it originated from a suspect’s camera. Our experimental results demonstrate the effectiveness of our approach.

A Stress Test for Robustness of Photo Response Nonuniformity (Camera Sensor Fingerprint) Identification on Smartphones

In the field of forensic imaging, it is important to be able to extract a camera fingerprint from one or a small set of images known to have been taken by the same camera (or image sensor). Note that we are using the word fingerprint because it is a piece of information extracted from images that can be used to identify an individual source camera. This technique is very important for certain security and digital forensic situations. Camera fingerprint is based on a certain kind of random noise present in all image sensors that is due to manufacturing imperfections and is, thus, unique and impossible to avoid. Photo response nonuniformity (PRNU) has become the most widely used method for source camera identification (SCI). In this paper, a set of attacks is designed and applied to a PRNU-based SCI system, and the success of each method is systematically assessed both in the case of still images and in the case of video. An attack method is defined as any processing that minimally alters image quality and is designed to fool PRNU detectors or, in general, any camera fingerprint detector. The success of an attack is assessed as the increment in the error rate of the SCI system. The PRNU-based SCI system was taken from an outstanding reference that is publicly available. Among the results of this work, the following are remarkable: the use of a systematic and extensive procedure to test SCI methods, very thorough testing of PRNU with more than 2000 test images, and the finding of some very effective attacks on PRNU-based SCI.

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.

PRNU Anonymous Algorithm Used for Privacy Protection in Biometric Authentication Systems

The photo response non-uniformity (PRNU) is used to connect an image to its source sensor. In this paper, researchers propose a PRNU anonymity method based on image segmentation to cut the relationship between the image and its source camera. According to the distribution rule of PRNU in the high and low frequency band of the image, the high and low frequency information of the part is also processed differently, which ensures the quality of the output image to a large extent. Experiments on the datasets show that the proposed method can preserve the biometric characteristics of the device while maintaining the anonymity of the device. Comparing with prior art, peak signal to noise ratio (PSNR) and cosine similarity are improved by 1.9 dB and 0.02 points, respectively.

PCEP: Few-Shot Model-Based Source Camera Identification

Source camera identification is an important branch in the field of digital forensics. Most existing works are based on the assumption that the number of training samples is sufficient. However, in practice, it is unrealistic to obtain a large amount of labeled samples. Therefore, in order to solve the problem of low accuracy for existing methods in a few-shot scenario, we propose a novel identification method called prototype construction with ensemble projection (PCEP). In this work, we extract a variety of features from few-shot datasets to obtain rich prior information. Then, we introduce semi-supervised learning to complete the construction of prototype sets. Subsequently, we use the prototype sets to retrain SVM classifiers, and take the posterior probability of each image sample belonging to each class as the final projection vector. Finally, we obtain classification results through ensemble learning voting. The PCEP method combines feature extraction, feature projection, classifier training and ensemble learning into a unified framework, which makes full use of image information of few-shot datasets. We conduct comprehensive experiments on multiple benchmark databases (i.e., Dresden, VISION and SOCRatES), and empirically show that our method achieves satisfactory performance and outperforms many recent methods in a few-shot scenario.

Portable immuno nano biosensor for multiple disease detection using 3D printed SPR technologies

The role of biosensors as point-of-care devices is well explored in recent years and their usage in the current market has been highly increased after the outbreak of the COVID pandemic. The point-of-care devices generally use sensing principles of electrochemical, immune fluorescence, microbial and electromagnetic. The sensor we developed uses the phenomenon of Surface Plasmon resonance with optic fiber technology which is a novel design to implement in point-of-care devices. With the latest innovations and improvements in 3D printing technologies the development and prototyping of these POC devices have been accelerated to the greatest potential. Here the entire device was prototyped using Creality Upgraded Ender 3 2021 model 3D printer where the 3D files were sliced in Ultimaker Cura software and printed with PLA filament by fused filament deposition modeling (FFDM). The device has a USB light and camera source on either side of the sensing chamber and a cassette with optic fiber coated with respective antibodies as a sensing platform. The device is connected to respective USB ports and introduced with the sample over a cassette and placed in the reaction chamber. The reaction occurs spontaneously which is captured by the webcam and recorded by the software. We tested the bare sensor with increased concentrations of glycerol after coating it with a silver mirror by a standard reproducible method of Tollens silver mirror. The sensor showed the best sensitivity with increasing concentrations of glycerol. The sensor also immobilized with target antibodies of pLDH parasite lactate dehydrogenase by non-specific binders just by adding target bio-analyte and receptor over silver mirror functionalized surface and tested with positive control antigen of malaria parasite antigen. The sensor showed the highest sensitivity at 50X concentration of antigen and least at 10x concentration stating maximum and minimum values for a limit of detection. This device using this principle could has wide functionalities to detect multiple biological samples by changing the antibodies to our respective analyte over the reaction chip for multiple times with low cost.

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.

Forensic Similarity for Source Camera Model Comparison

In the article 'Forensic Similarity for Digital Images', O. Mayer and M. C. Stamm introduce the forensic similarity approach, which aims at determining whether two image patches contain the same forensic traces or not. The proposed method is based on a feed-forward neural network which consists of two modules: a feature extraction module using a pair of CNNs in a siamese configuration, and a three-layer neural network that maps the extracted features into a similarity score. In this article, we explore the use of the forensic similarity score for source camera model comparison, as one of the possible applications of such an approach suggested by Mayer and Stamm.

Grassmannian learning mutual subspace method for image set recognition

This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it does not consider the variance of images in the set. To address this issue, we propose the Grassmannian learning mutual subspace method (G-LMSM), a NN layer embedded on top of CNNs that can process image sets more effectively and can be trained in an end-to-end manner. The image set is first represented by a low-dimensional input subspace and then this input subspace is matched with dictionary subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric. The key idea of G-LMSM is that the dictionary subspaces are learned as points on the Grassmann manifold, optimized with Riemannian stochastic gradient descent. This learning is stable, efficient and theoretically well-grounded. We demonstrate the effectiveness of our proposed method on hand shape recognition, face identification, and facial emotion recognition.

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.

Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems

In this article, we propose an integrity preservation analysis scheme of video contents working on the blockchain systems. The proposed scheme stores the core points of video contents analysis in the blockchain system permanently so that any user can easily verify the results of the proposed analysis procedure and their reliability. The scheme first examines the codec software characteristics of digital camera devices and video editing tools in advance, and stores them in the blockchain systems. Next, the scheme extracts the codec software characteristic from the target video file and compares it with the prepared characteristics in the blockchain system. With a matched characteristic, the scheme finds out the source camera device or the source video editing tool that generates the target video file. We also propose an integrity preservation trace scheme to record the transformation history of video contents into the blockchain systems. This scheme compares the original video and its transformed video frame by frame, and stores the comparison result with a hash value of the transformed video in the blockchain system. Then, the integrity analysis and transformation history of the target file can be easily searched in the blockchain system, where the hash value of the video file is used as the index of searching operation. We implement the proposed scheme into a practical tool upon a commercial blockchain system, Klaytn. Experimental results show that the proposed scheme carries out the integrity analysis of video contents with 100% accuracy, and provides the transformation history of non-original video contents with 100% accuracy when a proper parameter is given. It is also shown that the proposed scheme completes the integrity analysis within at most one second, and the search operation for transformation history within at most four seconds.