Inter-frame Forgery Detection Research Articles

Scene Change aware Inter-Frame Forgeries Detection Technique for Surveillance Videos Based on Similarities Analysis

Surveillance videos provide security and increases work efficiency in places of work and homes. as the most acceptable form of evidence, surveillance videos are now tampered to hide actions or convey wrong information. Researchers have proposed ways to mitigate the effect of activities of the attackers through checking the authenticity of the video. The proposed schemes suffer performance degradation in the presence of scene changes. Recently a scheme that addresses the effects of scene change on inter-frame forgery detection was developed where it detects scene changes and divides multiple scenes in to shots. The scheme improves the overall performance of the inter-frame forgery detection at the expense of high average computational time. In this research, a video scene change aware forgery detection scheme is proposed to mitigate the effect of scene change on inter-frame forgery detection with low average computational time. The proposed scheme utilizes the luminance level within frame region which is a more efficient feature to detect scene change. The experimental results show that the scheme has 57% decreases in computational average time and increased in accuracy to 99.03%.

Inter‐frame forgery detection and localisation in videos using earth mover's distance metric

Video forensics is one of the hot topics in multimedia forensics. Nowadays, spreading fake videos across the internet is becoming a profession mainly in politics and entertainment. In this study, a novel two-stage inter-frame video forgery detection technique is proposed. The first stage analyses spatio-temporal feature flow consistency to detect suspicious tamper points. Identifying the type of forgery and validating the recovered video are done in the second stage. Earth mover's distance is used as a similarity metric in both stages. The authors concentrate on a robust inter-frame forgery detection approach which can be applied for any challenging video. Compression at a higher rate, noise addition, and filtering are the anti-forensic tricks used by forgers to fool forensic techniques. However, most of the literature in video forgery detection has handled these issues as post-processing attacks and reported lesser accuracies for it. Hence the authors propose a robust and efficient forgery detection technique capable of identifying all kinds of inter-frame forgeries in videos. Experimental evaluation of the public video data set shows that the proposed approach outperforms existing approaches with an improved rate of robustness.

Exposing video inter-frame forgery via histogram of oriented gradients and motion energy image

Inter-frame forgery is a common type of video forgery to destroy the video evidence. It occurs in the temporal domain such as frame deletion, frame insertion, frame duplication, and frame shuffling. These forms of forgery are more frequently produced in a surveillance video because the camera position and the scene are relatively stable, where the tampering process is easy to operate and imperceptible. In this paper, we propose an efficient method for inter-frame forgery detection based on histogram of oriented gradients (HOG) and motion energy image (MEI). HOG is obtained from each image as a discriminative feature. In order to detect frame deletion and insertion, the correlation coefficients are used and abnormal points are detected via Grabb’s test. In addition, MEI is applied to edge images of each shot to detect frame duplication and shuffling. Experimental results prove that the proposed method can detect all inter-frame forgeries and achieve higher accuracy with lower execution time.

Low rank theory-based interframe forgery detection for blurry video

It is difficult to extract appropriate features of blurry digital video because of the low quality image for which traditional forgery detection methods are not effective. The video is often blurred due to various complicated conditions, such as limited bandwidth or low pixel complementary metal oxide semiconductor sensors. To address the feature extraction problem for blurry video, we explore one low rank theory to deblur video, which fuses multiple fuzzy kernels of key frame images by low rank decomposition. Further, we extract mean structural similarity features and information fidelity criterion features sequentially by the double detection mechanism to detect forgery points on blurred video frames. We can determine the forgery form of the video and locate the source replication area of the copy-move forgery. Experimental evaluation on two public video databases and videos that we recorded show that this method is robust in blurry video forgery detection, and compared with traditional video forgery detection methods, the efficiency is improved.

An Overview of Digital Video Tampering Detection Using Passive Methods and D-Hash Algorithm

Video tampering and integrity detection can be defined as methods of alteration of the contents of the video which will enable it to hide objects, an occasion or adjust the importance passed on by the collection of images in the video. Modification of video contents is growing rapidly due to the expansion of the video procurement gadgets and great video altering programming devices. Subsequently verification of video files is transforming into something very vital. Video integrity verification aims to search out the hints of altering and subsequently asses the realness and uprightness of the video. These strategies might be ordered into active and passive techniques. Therefore our area of concern in this paper is to present our views on different passive video tampering detection strategies and integrity check. Passive video tampering identification strategies are grouped into consequent three classifications depending on the type of counterfeiting as: Detection of double or multiple compressed videos, Region altering recognition and Video inter-frame forgery detection. So as to detect the tampering of the video, it is split into frames and hash is generated for a group of frames referred to as Group of Pictures. This hash value is verified by the receiver to detect tampering.

Detection of inter-frame forgeries in digital videos

Videos are acceptable as evidence in the court of law, provided its authenticity and integrity are scientifically validated. Videos recorded by surveillance systems are susceptible to malicious alterations of visual content by perpetrators locally or remotely. Such malicious alterations of video contents (called video forgeries) are categorized into inter-frame and intra-frame forgeries. In this paper, we propose inter-frame forgery detection techniques using tamper traces from spatio-temporal and compressed domains. Pristine videos containing frames that are recorded during sudden camera zooming event, may get wrongly classified as tampered videos leading to an increase in false positives. To address this issue, we propose a method for zooming detection and it is incorporated in video tampering detection. Frame shuffling detection, which was not explored so far is also addressed in our work. Our method is capable of differentiating various inter-frame tamper events and its localization in the temporal domain. The proposed system is tested on 23,586 videos of which 2346 are pristine and rest of them are candidates of inter-frame forged videos. Experimental results show that we have successfully detected frame shuffling with encouraging accuracy rates. We have achieved improved accuracy on forgery detection in frame insertion, frame deletion and frame duplication.

Inter-frame passive-blind forgery detection for video shot based on similarity analysis

Frame insertion, deletion and duplication are common inter-frame tampering operations in digital videos. In this paper, based on similarity analysis, a passive-blind forensics scheme for video shots is proposed to detect inter-frame forgeries. This method is composed of two parts: HSV (Hue-Saturation-Value) color histogram comparison and SURF (Speeded Up Robust Features) feature extraction together with FLANN (Fast Library for Approximate Nearest Neighbors) matching for double-checking. We mainly calculate H-S and S-V color histograms of every frame in a video shot and compare the similarity between histograms to detect and locate tampered frames in the shot. Then we utilize SURF feature extraction and FLANN matching to further confirm the forgery types in the tampered locations. Experimental results demonstrate that the proposed detection method is efficient and accurate in terms of forgery identification and localization. In contrast to other inter-frame forgery detection methods, our scheme can detect three kinds of forgery operations and has its own superiority and applicability as a passive-blind detection method.

Video Inter-frame Forgery Detection: A Survey

Objectives: This paper summarizes all the proposed techniques involved in digital video inter-frame forgery detection for MPEG-1, 2, 4 and H.264/AVC encoded videos. Methods/Statistical Analysis: Double compression detection techniques are classified here on the basis of footprints analyzed during detection. The detection methods designed for videos that use fixed GOP structure for first and any of the subsequent compression are different from the videos that use different GOP structure. Video inter-frame forgery detection techniques are then analyzed on the basis of type of forgery they detect and the type of codec used for video encoding. Findings: Digital videos often provide forensic evidence in legal, medical and surveillance applications but are more prone to inter-frame forgeries, which are not only easy to perform but are equally difficult to detect as well. The analysis of the literature ascertained that majority of the proposed techniques are dependent on the number of frames tampered and video codec used to encode the videos. Among these proposed techniques, double MPEG compression was best detected using the technique which utilizes Benford’s law and was proposed by Chen and Shi on MPEG-1 and MPEG-2 encoded videos. On the other hand, Wang et al. gave sound results for all kinds of inter-frame forgeries on MPEG-2 encoded videos by utilizing the measure of optical flow consistency. Since very few authors focussed on forgery detection in MPEG-4 encoded videos and thereby such techniques have not been discussed in many survey papers. Moreover, digital cameras especially surveillance cameras which generate massive amount of videos these days have built-in MPEG-4 codec because it offers a better compression rate. Application/Improvements: Video forensics domain, therefore, is in dire need of a technique that will detect any kind of inter-frame forgery in MPEG-4 encoded videos.