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In a novel method for identifying the source camera of a digital image is proposed. The method is based on first extracting imaging sensor's pattern noise from many images and later verifying its presence in a given image through a correlative procedure. In this paper, we investigate the performance of this method in a more realistic setting and pr...
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... fore, the question to be answered is if the decrease in false- positive rate can compensate for the decrease in the true de- tection rate. The results corresponding to the sensor pattern noise based method and the proposed combined decision pro- cess are given in Figure 6. Here, the combined approach is compared to sensor noise based detection in terms of its ac- curacy which is defined as the ratio of the number of correct decisions to overall number of decisions. ...
Citations
... Over the past decade, substantial progress has been made in source identification through the extraction of the PRNU fingerprint from images [2][3][4][5][6][7][8][9][10]. The pioneering work of Lukas et al. [2] has greatly contributed to the advancement of research in this field. ...
... The pioneering work of Lukas et al. [2] has greatly contributed to the advancement of research in this field. Many contemporary studies estimate PRNU as the residual noise obtained by subtracting a denoised image from the original image [2][3][4][5][6][7]. Researchers have also strived to enhance the accuracy of source identification by mitigating undesired artifacts such as scene details [7] and irrelevant noise, thereby improving the quality and reliability of PRNU fingerprints derived from images [8][9][10]. ...
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.
... Scheme of denoising images and classification of a new image[42] ...
In this paper we deal with the issue of digital camera identification (DCI) based on images. This area matches the digital forensics (DF) research. This topic has attracted many researchers and number of algorithms for DCI have been proposed. However, majority of them focus only on camera identification with high accuracy without taking into account the speed of image processing. In this paper we propose an effective algorithm for much faster camera identification than state-of-the-art algorithms. Experimental evaluation conducted on two large image datasets including almost 14.000 images confirms that the proposed algorithm achieves high classification accuracy of 97 [%] in much shorter time compared with state-of-the-art algorithms which obtained 92.0 − 96.0 [%]. We also perform a statistical analysis of obtained results which confirms their reliability.
... Dataset I First dataset consist of modern devices such as smartphones, drones, compact cameras or DSLRs. The following devices were used: smartphones (Acer Liquid Jade S, Apple iPhone 5S, LG K10, Samsung Galaxy S7 and Samsung Galaxy Tab A (2016) tablet); [31] compact cameras: Canon SX160, Canon SX270, Nikon P100); drones (DJI Spark, Yuneec Breeze 4K) and two DSLRs (Nikon D3100 and D7200). All devices contain CMOS imaging sensor expect Canon SX160 (CCD). ...
In this paper we consider the problem of a privacy threat enabling tracing digital cameras by the analysis of pictures they produced. As thousands of images are processed at a mass scale, the threat may apply to most users of digital cameras. We consider a state-of-the-art algorithm for digital camera identification proposed in Lucas et al. (IEEE Trans Inf Forensics Secur 1(2):205–214, 2006) and discuss strategies that can be used to bypass it, in order to make information about the camera unavailable. It turns out that many natural strategies like Gaussian blur, adding artificial noise or removing pixels’ least significant bit from the image does not prevent the identification of a camera unless a huge loss of image details is suffered. On the other hand, we show a method to bypass the camera identification with a just marginally more complex, yet not intuitive, method namely cropping the image on the edges and resizing to the original size using Lanczos resampling.
... So far, most of the work has focused on either images or videos (but not both). For images, much work has been done to improve PRNU based attribution [11,17,18,19,20]. Many researchers have also extended image-centric methods towards video [21,22,23,24,25,26,27,28,29]. ...
Photo Response Non-Uniformity (PRNU) based source camera attribution is an effective method to determine an image or a video’s origin camera. However, modern devices, especially smartphones, capture images and videos at different resolutions using the same sensor array, PRNU attribution can become ineffective as the camera fingerprint and query object can be misaligned. While capturing visual objects (either image or video), cameras may use different in-camera operations as well as they may use different parts of the sensor. In this paper, we investigate the problem of source camera attribution of a visual object by doing a thorough investigation of a comprehensive dataset, NYUAD Mixed Media Dataset. This investigation takes many factors into accounts, such as the fact that visual objects may have been captured using different resolution and aspect ratios. Furthermore, the visual objects may use different regions of the sensor, including the usage of boundary pixels for videos. Taking these various cases into account, we propose an efficient search which not only gives the state-of-the-art results but also performs significantly faster compared to existing methods.
... For images, the PRNU-based method has been well studied. Following the seminal work in [1], much research has been done to improve the scheme [1], [17]- [20], and also make camera identification effective in practical situations [2], [3], [5], [6], [21]. Researchers have also studied the effectiveness of the PRNU-based method by proposing various counter forensics and anti-counter-forensics methods [22], [23]. ...
... Since severe compression will destroy the spatial correlation properties between pixels caused by CFA interpolation, the method can not apply to severely compressed images. In order to solve the problem, Bayram improved the method [27] by extracting the periodicity of the second-order derivatives. Long et al. [16] proposed a second-order model based on pixel correlation, and then estimated the CFA interpolation coefficients for each color channel and sent them to the BP neural network for classification. ...
Source camera identification, which means identifying the camera source of a given image, has become one of the most important branches of digital image forensics. In order to improve the detection accuracy, the feature dimensions used in existing methods are increasing, and consequently Support Vector Machine (SVM) seems no longer applicable. In this paper, an ensemble classifier is introduced into to source camera identification, which uses the fusion features to capture software-related, hardware-related, and statistical characteristics left on the images by digital camera. Experimental results indicate that the proposed method can achieve near 100% accuracy for camera brand and model identification, and also outperforms the baseline methods in identifying different camera individuals.
... PRNU (Photo Response Non-Uniformity) noise based source camera attribution is a well established method in media forensics [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. However, when identification has to be done using a potentially large set of candidate source cameras, PRNU-based matching can be computationally expensive as the PRNU from the query image has to be correlated against every camera fingerprint in the database. ...
... The authors propose sparse 3d transform-domain collaborative filtering for more accurate noise extraction. According to Sutcu et al. [35], decision process can be enhanced by including camera's demosaicing characteristics. An effect of scenes and edges is suppressed by edge adaptive PRNU predictor proposed in [21] and [36]. ...
... To improve the accuracy further, various enhancements are proposed as discussed in introduction section of this paper. In one such enhancement, Sutcu et al. [35] propose to use PRNU based camera identification method along with camera identification method based on CFA interpolation artifacts [1]. According to authors, verifying the consistency of demosaicing artifacts can further lead to improvement in identification accuracy. ...
... According to authors, verifying the consistency of demosaicing artifacts can further lead to improvement in identification accuracy. Therefore to show PRNU estimated by proposed algorithm can also be used with various enhancements techniques, we perform experiments using model proposed in [35]. In [35], authors use two-step verification for identifying the acquisition device of the image. ...
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.
... PRNU noise-based source attribution is a well studied area [7]. After the seminal work in [1], significant research has been carried out to strengthen this scheme [8,9]. Some work has also focused on attacking the scheme [10]. ...
Photo Response Non-Uniformity (PRNU) noisebased source attribution is a well known technique to verify the camera of an image or video. Researchers have proposed various countermeasures to prevent PRNU-based source camera attribution. Forced seam-carving is one such recently proposed counter forensics technique. This technique can disable PRNUbased source camera attribution by forcefully removing seams such that the size of most uncarved image blocks is less than 50 × 50 pixels. In this paper, we show that given multiple seamcarved images from the same camera, source attribution can still be possible even if the size of uncarved blocks in the image is less than the recommended size of 50 × 50 pixels. Theoretical analysis and experiments with multiple cameras demonstrate that the effectiveness of our scheme depends on the number of seams carved from an image and the randomness of the seam positions.
... These methods are not applicable for image having no pixel defects in the sensor. Sutcu et al [3] proposed that after getting the noise residue of each image using the denoising filter, the residue may contain the some of the scene details. In order to eliminate those details to increase the accuracy of the getting the reference pattern which will be used to test against the test samples, those details have to reduced or eliminated from the noise residue. ...
The sensor pattern noise is associated with digital images, due to imperfection in the chip of image sensor manufacturing process and it causes pixel sensitivity variation in the imaging sensor. The distinct property of these pattern noises makes it unique to that image sensor. Therefore, it acts as 'fingerprint' of that particular imaging sensor. The main contributor of sensor pattern noise is Photo Response Non-Uniformity noise (PRNU). In this proposed work, we analyse the PRNU estimation and enhance the content of the PRNU for better accurate identification of the source camera. The PRNU extraction consists of three stages: filtering, estimation and enhancement stage. Each stage consists of various techniques incorporated for the PRNU extraction. The experiments were conducted on natural images taken from the different camera models. For our experiment, 300 images from 6 different camera models are used and identification of source camera of a given image is done by correlating the PRNU reference pattern with the noise residual model obtained from the test image. Index Terms-sensor pattern noise, photo-response non-uniformity, noise residual, and reference pattern.
