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

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

Detecting Manipulations in Video

This chapter presents the techniques researched and developed within InVID for the forensic analysis of videos, and the detection and localization of forgeries within User-Generated Videos (UGVs). Following an overview of state-of-the-art video tampering detection techniques, we observed that the bulk of current research is mainly dedicated to frame-based tampering analysis or encoding-based inconsistency characterization. We built upon this existing research, by designing forensics filters aimed to highlight any traces left behind by video tampering, with a focus on identifying disruptions in the temporal aspects of a video. As for many other data analysis domains, deep neural networks show very promising results in tampering detection as well. Thus, following the development of a number of analysis filters aimed to help human users in highlighting inconsistencies in video content, we proceeded to develop a deep learning approach aimed to analyze the outputs of these forensics filters and automatically detect tampered videos. In this chapter, we present our survey of the state of the art with respect to its relevance to the goals of InVID, the forensics filters we developed and their potential role in localizing video forgeries, as well as our deep learning approach for automatic tampering detection. We present experimental results on benchmark and real-world data, and analyze the results. We observe that the proposed method yields promising results compared to the state of the art, especially with respect to the algorithm’s ability to generalize to unknown data taken from the real world. We conclude with the research directions that our work in InVID has opened for the future

Detecting Tampered Videos with Multimedia Forensics and Deep Learning

© 2019, Springer Nature Switzerland AG. User-Generated Content (UGC) has become an integral part of the news reporting cycle. As a result, the need to verify videos collected from social media and Web sources is becoming increasingly important for news organisations. While video verification is attracting a lot of attention, there has been limited effort so far in applying video forensics to real-world data. In this work we present an approach for automatic video manipulation detection inspired by manual verification approaches. In a typical manual verification setting, video filter outputs are visually interpreted by human experts. We use two such forensics filters designed for manual verification, one based on Discrete Cosine Transform (DCT) coefficients and a second based on video requantization errors, and combine them with Deep Convolutional Neural Networks (CNN) designed for image classification. We compare the performance of the proposed approach to other works from the state of the art, and discover that, while competing approaches perform better when trained with videos from the same dataset, one of the proposed filters demonstrates superior performance in cross-dataset settings. We discuss the implications of our work and the limitations of the current experimental setup, and propose directions for future research in this area

Video copy-move forgery detection scheme based on displacement paths

Sophisticated digital video editing tools has made it easier to tamper real videos and create perceptually indistinguishable fake ones. Even worse, some post-processing effects, which include object insertion and deletion in order to mimic or hide a specific event in the video frames, are also prevalent. Many attempts have been made to detect such as video copy-move forgery to date; however, the accuracy rates are still inadequate and rooms for improvement are wide-open and its effectiveness is confined to the detection of frame tampering and not localization of the tampered regions. Thus, a new detection scheme was developed to detect forgery and improve accuracy. The scheme involves seven main steps. First, it converts the red, green and blue (RGB) video into greyscale frames and treats them as images. Second, it partitions each frame into non-overlapping blocks of sized 8x8 pixels each. Third, for each two successive frames (S2F), it tracks every block’s duplicate using the proposed two-tier detection technique involving Diamond search and Slantlet transform to locate the duplicated blocks. Fourth, for each pair of the duplicated blocks of the S2F, it calculates a displacement using optical flow concept. Fifth, based on the displacement values and empirically calculated threshold, the scheme detects existence of any deleted objects found in the frames. Once completed, it then extracts the moving object using the same threshold-based approach. Sixth, a frame-by-frame displacement tracking is performed to trace the object movement and find a displacement path of the moving object. The process is repeated for another group of frames to find the next displacement path of the second moving object until all the frames are exhausted. Finally, the displacement paths are compared between each other using Dynamic Time Warping (DTW) matching algorithm to detect the cloning object. If any pair of the displacement paths are perfectly matched then a clone is found. To validate the process, a series of experiments based on datasets from Surrey University Library for Forensic Analysis (SULFA) and Video Tampering Dataset (VTD) were performed to gauge the performance of the proposed scheme. The experimental results of the detection scheme were very encouraging with an accuracy rate of 96.86%, which markedly outperformed the state-of-the-art methods by as much as 3.14%