Sensor Pattern Noise Extraction Research Articles

An improved surveillance video forgery detection technique using sensor pattern noise and correlation of noise residues

Digital videos have become an important aspect of our lives lately, from a personal memorable to surveillance videos which can be presented in a court as an evidence now, this video evidence can be very important for the court of law and the investigators to understand the events as they occurred. Huge inventions in the video editing world have resulted in a margin of doubt, to the point where courts are now reluctant to accept any video evidence in to their case files, as for a digital video to be presented as an evidence, it’s integrity and authenticity is very important. This progression in technology can push the courts to a point where they refuse to trust anything that was caught on a video. Thus, more efforts have to be made in the video forensics world to avoid such a future. It has been shown that sources induce their specific noise patterns (i.e. Sensor pattern noise) to the captured media, and these patterns can be used to trace the source sensor and also to detect any forgeries. Acquiring SPN (sensor pattern noise) consists of two steps, first to get noise residues by subtracting denoised frames from the actual frames taken from the sample data and then to average those noise residues to get the sensor pattern noise. Problem arises when the attacker can also extract sensor pattern noise or has access to it. The attacker can induce SPN in forged frames which will prevent detection using the previous SPN based detection technique. This paper first proposes how the attacker can compensate a forged image by inducing SPN in it and then proposes a forgery detection technique for such a scenario which would not only rely on noise residue correlation with SPN but correlation with noise residue from the previous frame as well. We first estimate sensor pattern noise based on locally adaptive discrete cosine transform, then we correlate noise residue of frame n under investigation with the sensor pattern noise and with the noise residue of the previous frame n-1 as well. Now, even if the attacker compensates the forged frame by inducing SPN in it, the inter-frame continuity of noise shall be disturbed hence identified. Simulations using benchmark dataset for SPN extraction technique and custom dataset for video forgery detection, show that this methodology successfully detects any forgeries and locate their positions in the normal case and in the case where SPN is induced in the forged frames as well.

Improving source camera identification performance using DCT based image frequency components dependent sensor pattern noise extraction method

Sensor imperfections in the form of photo response non-uniformity (PRNU) are widely used to perform various image forensic tasks such as source camera identification, image integrity verification, and device linking. The PRNU contains important information about the sensor in terms of frequency contents, this information makes it suitable for various image forensic applications. The main drawback of existing methods of PRNU extraction is that the extracted PRNU contains fine details of the image i.e., the high-frequency details (edges and texture). For solving this problem we have applied a pre-processing step on widely accepted PRNU extraction methods. Our pre-processing step is based on the fact that ‘PRNU is a very weak noise signal and hence it can be efficiently extracted from the image by applying PRNU extraction method in low frequency (LF) and high frequency (HF) components of the image separately’. Initially, we have applied this pre-processing concept to the widely accepted PRNU extraction methods and found that it is able to improve the performance of most of the PRNU extraction methods. The best improvement takes place for Mihcak filter. Hence in the remaining part of the work, this generalized concept is more precisely applied to the Mihcak filter only. By utilizing the proposed pre-processing idea with the Mihcak filer, the new filter is termed as the pMihcak filter. PRNU extracted using pMihcak filter contains the least amount of HF details of the image. Also, the pMihcak filter is able to extract PRNU from low-frequency components of the image which otherwise not possible for existing PRNU extractors.

Suppressing Random Artifacts in Reference Sensor Pattern Noise via Decorrelation

Photo response nonuniformity noise is a component of sensor pattern noise (SPN) of digital cameras. It characterizes the imaging devices of cameras and has been widely used in source camera identification (SCI). However, the reference SPN always be contaminated by unwanted nonunique artifacts. Previous works reported that these nonunique artifacts were periodic signals shared among cameras, which undergo the same or similar in-camera processing procedures. In this letter, we first illustrate that these nonunique artifacts, in addition to periodic signals, also contain random signals caused by the imperfection of SPN extraction method. To improve the quality of SPN and the performance of SCI, then we propose a scheme to suppress these random signals by decorrelation. In our proposed method, the reference SPN is modeled as a white Gaussian noise, while the random signals introduced by SPN extraction method are correlated noises, we suppress these correlated components to purify the SPN and enhance the performance of SCI. The experimental results on “Dresden” database show that the proposed scheme achieves better receiver operating characteristic curves than the state-of-the-art methods.

Identifying source camera using guided image estimation and block weighted average

Sensor pattern noise (SPN) has been widely used in source camera identification. However, the SPN extracted from natural image may be contaminated by its content and eventually introduce side effect to the identification accuracy. In this paper, an effective source camera identification scheme based on guided image estimation and block weighted average is proposed. Before the SPN extraction, an adaptive SPN estimator based on image content is implemented to reduce the influence of image scene and improve the quality of the SPN. Furthermore, a novel camera reference SPN construction method is put forward by using some ordinary images, instead of the blue sky images in the previous schemes, and a block weighted average approach is used to suppress the influence of the image scenes in the reference SPN. Experimental results and analysis indicate that the proposed method can effectively identify the source of the natural image, especially in actual forensics environment with a small number of images.

Identification of smartphone brand and model via forensic video analysis

Recording videos on smartphones and other mobile devices, given their enormous popularity, is currently very common. The portability of these devices facilitates their use for recording videos in a wide variety of situations, including while witnessing criminal activities. These videos can be later used as evidence in legal proceedings. Therefore, the forensic analysis of videos taken with mobile device videos is important, and could serve for legal and also investigative purposes. It is necessary, however, to use techniques that are quite specific to this type of devices, given some peculiar features of their cameras. In this paper, we will address the issue of video source acquisition identification by presenting a technique based on sensor noise and wavelet transform extraction from video key frames. These frames are extracted using an efficient algorithm that takes their content into account, improving the selection of frames to be analyzed over past proposals. The scheme presented consists of four stages: (1) Key frames extraction, (2) sensor pattern noise extraction, (3) feature extraction, and (4) classifier training and prediction. We also present experimental results that support the validity of the techniques used and show promising results.

Empirical investigation into the correlation between vignetting effect and the quality of sensor pattern noise

The sensor pattern noise (SPN) is a unique attribute of the content of images that can facilitate identification of source digital imaging devices. Owing to its potential in forensic applications, it has drawn much attention in the digital forensic community. Although much work has been done on the applications of the SPN, investigations into its characteristics have been largely overlooked in the literature. In this study, the authors aim to fill this gap by providing insight into the characteristic dependency of the SPN quality on its location in images. They have observed that the SPN components at the image periphery are not reliable for the task of source camera identification, and tend to cause higher false-positive rates. Empirical evidence is presented in this work. The authors suspect that this location-dependent SPN quality degradation has strong connection with the so-called ‘vignetting effect’, as both exhibit the same type of location dependency. The authors recommend that when image blocks are to be used for forensic investigations, they should be taken from the image centre before SPN extraction is performed in order to reduce false-positive rate.