A PatchMatch-based Dense-field Algorithm for Video Copy-Move Detection and Localization

We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions

Video Anti Forensics - A Review

In the recent years the availability of the digital multimedia devices (such as cameras, mobile-phones, digital recorders, etc.) has increased rapidly. Digital photos have been widely used as historical records and as evidences of real happenings in applications from journalist reporting, police investigation, law enforcement, insurance, medical and dental examination, military, and museum to consumer photography. Forensic investigation endeavors to use science to uncover the transferred evidence and discern its meaning. The examination requires that the evidence be reliable and accurate to ensure a correct outcome. However, criminals may use anti-forensic methods to work against the process or interfere with the evidence itself. In this paper different techniques of anti-forensic are explained. Each of these proposed techniques accounts for distinct actions that compromise the availability or usefulness of evidence to the forensic process. DOI: 10.17762/ijritcc2321-8169.16047

Digital Multimedia Forensics and Anti-Forensics

As the use of digital multimedia content such as images and video has increased, so has the means and the incentive to create digital forgeries. Presently, powerful editing software allows forgers to create perceptually convincing digital forgeries. Accordingly, there is a great need for techniques capable of authenticating digital multimedia content. In response to this, researchers have begun developing digital forensic techniques capable of identifying digital forgeries. These forensic techniques operate by detecting imperceptible traces left by editing operations in digital multimedia content. In this dissertation, we propose several new digital forensic techniques to detect evidence of editing in digital multimedia content. We begin by identifying the fingerprints left by pixel value mappings and show how these can be used to detect the use of contrast enhancement in images. We use these fingerprints to perform a number of additional forensic tasks such as identifying cut-and-paste forgeries, detecting the addition of noise to previously JPEG compressed images, and estimating the contrast enhancement mapping used to alter an image. Additionally, we consider the problem of multimedia security from the forger's point of view. We demonstrate that an intelligent forger can design anti-forensic operations to hide editing fingerprints and fool forensic techniques. We propose an anti-forensic technique to remove compression fingerprints from digital images and show that this technique can be used to fool several state-of-the-art forensic algorithms. We examine the problem of detecting frame deletion in digital video and develop both a technique to detect frame deletion and an anti-forensic technique to hide frame deletion fingerprints. We show that this anti-forensic operation leaves behind fingerprints of its own and propose a technique to detect the use of frame deletion anti-forensics. The ability of a forensic investigator to detect both editing and the use of anti-forensics results in a dynamic interplay between the forger and forensic investigator. We use develop a game theoretic framework to analyze this interplay and identify the set of actions that each party will rationally choose. Additionally, we show that anti-forensics can be used protect against reverse engineering. To demonstrate this, we propose an anti-forensic module that can be integrated into digital cameras to protect color interpolation methods

Autoencoder with recurrent neural networks for video forgery detection

Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.Comment: Presented at IS&T Electronic Imaging: Media Watermarking, Security, and Forensics, January 201

Video Forgery Detection: A Comprehensive Study of Inter and Intra Frame Forgery With Comparison of State-Of-Art

Availability of sophisticated and low-cost smart phones, digital cameras, camcorders, surveillance CCTV cameras are extensively used to create videos in our daily life. The prevalence of video sharing techniques presently available in the market are: YouTube, Facebook, Instagram, snapchat and many more are in utilization to share the information related to videos. Besides this, there are many software which can edit the content of video: Window Movie Maker, Video Editor, Adobe Photoshop etc., with this available software anyone can edit the video content which is called as “Forgery” if edited content is harmful. Usually, videos play a vital role in terms of proof in crime scene. The Victim is judged by the proof submitted by the lawyer to the court. Many such cases have evidenced that the video being submitted as proof is been forged. Checking the authentication of the video is most important before submitting as proof. There has been a rapid development in deep learning techniques which have created deepfake videos where faces are replaced with other faces which strongly made a belief of saying “Seeing is no longer believing”. The available software which can morph the faces are FakeApp, FaceSwap etc., the increased technology really made the Authentication of proofs very doubtful and un-trusty which are not accepted as proof without proper validation of the video. The survey gives the methods that are capable of accurately computing the videos and analyses to detect different kinds of forgeries. It has revealed that most of the existing methods are relying on number of tampered frames. The proposed techniques are with compression, double compression codec videos where research is being carried out from 2016 to present. This paper gives the comprehensive study of techniques, algorithms and applications designed and developed to detect forgery in videos

Video Inter-frame Forgery Detection Approach for Surveillance and Mobile Recorded Videos

We are living in an age where use of multimedia technologies like digital recorders and mobile phones is increasing rapidly. On the other hand, digital content manipulating softwares are also increasing making it easy for an individual to doctor the recorded content with trivial consumption of time and wealth. Digital multimedia forensics is gaining utmost importance to restrict unethical use of such easily available tampering techniques. These days, it is common for people to record videos using their smart phones. We have also witnessed a sudden growth in the use of surveillance cameras, which we see inhabiting almost every public location. Videos recorded using these devices usually contains crucial evidence of some event occurence and thereby most susceptible to inter-frame forgery which can be easily performed by insertion/removal/replication of frame(s). The proposed forensic technique enabled detection of inter-frame forgery in H.264 and MPEG-2 encoded videos especially mobile recorded and surveillance videos. This novel method introduced objectivity for automatic detection and localization of tampering by utilizing prediction residual gradient and optical flow gradient. Experimental results showed that this technique can detect tampering with 90% true positive rate, regardless of the video codec and recording device utilized and number of frames tampered

Recent Advances in Digital Image and Video Forensics, Anti-forensics and Counter Anti-forensics

Image and video forensics have recently gained increasing attention due to the proliferation of manipulated images and videos, especially on social media platforms, such as Twitter and Instagram, which spread disinformation and fake news. This survey explores image and video identification and forgery detection covering both manipulated digital media and generative media. However, media forgery detection techniques are susceptible to anti-forensics; on the other hand, such anti-forensics techniques can themselves be detected. We therefore further cover both anti-forensics and counter anti-forensics techniques in image and video. Finally, we conclude this survey by highlighting some open problems in this domain

Detection of video frame insertion based on constraint of human visual perception

Recently, due to availability of inexpensive and easily-operable multimedia tools, digital multimedia technology has experienced drastic advancements. At the same time, video forgery becomes much easier and makes more difficult to validate the video content. Consequently, the origin and integrity of video can no longer be taken for granted. A methodology is developed that is capable of detecting the video frame insertion based on the constraint of human visual perception. The main idea is based on the so-called differential sensitivity. That is, that the variation of brightness of neighboring video frames has some constraint. First, the video sequence is partitioned into short and overlapping sub-sequences. Second, the ratio of the temporal variation of brightness calculated at the beginning and the ending frames of each sub-sequence is computed and compared with a threshold to determine the approximate location of the video frame insertion. Third, a procedure is conducted to determine the exact location of the insertion. The success of simulation works on more than 200 video sequences. The precision rate of detection is about 94.09%, and the precision rate of detecting location of frame insertion is 84.88% on testing databas