Video Forgery Research Articles

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 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.

Video forgery detection based-on passive (blind) approach

Video forgery detection based-on passive (blind) approach

DNN Based Moth Search Optimization for Video Forgery Detection

Recently, video tampering process becomes easier due to the rapid advancements in user-friendly editing software and multimedia technology (e.g., Mokey by Imagineer Systems, and Photoshop and Premiere by Adobe). This technologies may highly tamper the original videos, so that the audience gets mislead. Nowadays, MPEG-4 codec is included in a large proportions of video cameras and surveillance systems. Therefore the double compression detection process included as an initial step in the video forensic is receiving a high significance. In this paper, the double compression artifacts is detected by adopting the Markov based features, which identifies the interpolated original videos. The double compressed frames are then segmented by introducing an SLIC super pixel segmentation technique. Here, the feature extraction is performed by applying the scale information that is obtained from the multi-scale Gabor filters. The features of this Gabor scale accurately extract the structural features and also reduce too much of redundancy. This extracted features are then provided to DNN (deep neural network) for forgery detection. In this video forensic process, DNN classifier is included for forgery detection. The CNN classifier is included in various existing forgery detection techniques. But, in our work we include DNN because it contains number of hidden layers which provide accurate results for this forgery detection process. To improve the DNN performance, Moth Search Optimization (MSO) algorithm is introduced in this forgery detection technique. Every nook and corner of this world we can able to find the surveillance cameras for security purpose. But, some fraudsters perform forgeries in this recorded videos for their own benefits. To identify this, a lot of forgery detection techniques are coming into existence. So in this work, we introduce the DNN based MSO to perform the forgery detection in videos.This implementation is processed in python simulation platform. The parametric evaluations are taken in terms of F1-Score, average accuracy, Precision, Recall and. Experimental results will provide improved performance in video forgery detection.

A Review : Video Tampering Attacks and Detection Techniques

Technological advancements of various video and image editing tools has reached such a level that the tampering of digital video or image can be performed easily without degrading their quality or leaving any visual evidence. This review paper presents an overview of various types of video forgery and the different types of techniques that are employed for its detection. Passive and active forgery detection techniques are commonly used methods for detecting the tampering in a digital video. Passive and active tampering detection techniques are utilized for detecting the integrity as well as the authenticity of a given video. The aim of this review is to provide some productive information about video tampering attacks for upcoming researchers.

Differentiating synthetic and optical zooming for passive video forgery detection: An anti-forensic perspective

A video can be manipulated using synthetic zooming without using the state-of-the-art video forgeries. Synthetic zooming is performed by upscaling individual frames of a video with varying scale factors followed by cropping them to the original frame size. These manipulated frames resemble genuine natural (optical) camera zoomed frames and hence may be misclassified as a pristine video by video forgery detection algorithms. Even if such a video is classified as forged, forensic investigators may ignore the results, believing it as part of an optical camera zooming activity. Hence, this can be used as an anti-forensic method which eliminates digital evidence. In this paper, we propose a method for differentiating optical camera zooming from synthetic zooming for video tampering detection. The features used for this method are pixel variance correlation and sensor pattern noise. Experimental results on a dataset containing 3200 videos show the effectiveness of the proposed method.

An approach to detect video frame deletion under anti-forensics

As a simple yet effective operation, frame deletion is widely used in video forgery. Many video forensic techniques have been developed to detect this manipulation. Some inter-frame continuity based methods are capable to detect frame deletion as well as locate the frame deletion points precisely. However, due to the simple principle, this kind of inter-frame continuity based methods are vulnerable to anti-forensic strategies. In this paper, we first put forward an inter-frame interpolation as an anti-forensic operation which is easy to realize. Then, to detect video frame deletion under anti-forensics, the new artifacts introduced by interpolation are analyzed, and we present a global and local joint feature to distinguish the interpolated frames and pristine frames. The feature overcomes the problem of weak residual in HEVC videos, and low dimension ensures the real-time detection. Experimental results show that the anti-forensic view we proposed needs to be considered in frame deletion forensics. In addition, the proposed global and local joint feature can detect frame deletion under anti-forensic operation effectively.

Inter‐frame forgery detection based on differential energy of residue

Inter-frame forgery marks a central type of forgery in surveillance videos, and involves three aspects - frame duplication, insertion, and deletion - under temporal domain. However, this forgery type has received little attention from scholars. More efforts have been on detecting only a single aspect of inter-frame forgery. Furthermore, studies have confirmed that previous methods did not achieve high accuracy for all forgeries types with low computational loads at the same time. In this study, the proposed method establishes a framework that can simultaneously detect all aspects of inter-frame forgeries. During the decoding process, the authors extract residue data of each frame from a video stream. Then spatial and temporal energies are exploited to illustrate data flow, and abnormal points are determined to detect forged frames. Noise ratios of forged and original frames are estimated for differentiating insertion from duplication attacks. Experimental results indicate that the proposed method achieves higher accuracy and lower computational time for detecting inter-frame forgery.

Digital Video Source Acquisition Forgery Technique Based on Pattern Sensor Noise Extraction

Digital camera of a smartphone is a component frequently used by people to capture a large number of videos, which can illustrate situations that compromise their presumption of innocence in court cases. Usually, these videos circulate on the Internet, prone to intentional manipulation to prosecute one person or exempt another. In this sense, digital videos are a matter of great importance for forensic science, because they can be useful to verify the authenticity of such evidence in judicial processes, helping to make sound decisions. However, it is possible that criminals or attackers know weaknesses of forensic techniques and use anti-forensic techniques to manipulate videos without leaving any trace of the procedure performed. Forensic science confronts anti-forensic techniques, analyzes them rigorously and applies anti-measures in the development of techniques to detect anti-forensic operations. In this paper, anti-forensic techniques are proposed to perform the source anonymization and forgery in MP4 videos.

AN IMPROVED ACTION KEY FRAMES EXTRACTION ALGORITHM FOR COMPLEX COLOUR VIDEO SHOT SUMMARIZATION

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems

Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive Frames

Now-a-days, videos can be easily recorded and forged with user-friendly editing tools. These videos can be shared on social networks to make false propaganda. During the process of spa ...

Video frame and region duplication forgery detection based on correlation coefficient and coefficient of variation

In this paper, we present a passive blind scheme consisting of two different algorithms to detect frame and region duplication forgeries in videos. We have examined the video frame duplication forgery in three different forms such as duplication of a sequence of consecutive video frames at long continue running position, duplication of many such sequences having different lengths at many different locations and duplication from other videos having different and same dimensions which can raise a serious problem in the real world scenario. The algorithm I of proposed scheme has detected these three different forms of copy-moved frame duplication forgery in videos by obtaining the mean features of each video frame for evaluating the correlation between sequences. In this paper, we have also analysed forged regular and irregular region within same frame at different locations and from other frame to one or more sequences of consecutive frames of the same video at same locations. It creates a challenge to detect this copy-move forgery due to slightly change in pixel intensity values in the duplicated region and providing high correlation as authentic region. The algorithm II of proposed scheme has detected these copy-moved region duplication forgeries in videos by locating the position of error with threshold process in order to calculate the similarities between regions of two frames or within affected frame. In this paper, the experimental results show the higher detection accuracy and execution time efficiency of proposed scheme than the latest algorithms with satisfactory performance.

Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames

With the immensely growing rate of cyber forgery today, the integrity and authenticity of digital multimedia data are highly at stake. In this work, we deal with forensic investigation of cyber forgery in digital videos. The most common types of inter-frame forgery in digital videos are frame insertion, deletion and duplication attacks. A number of significant researches have been carried out in this direction, in the past few years. In this paper, we propose a two-step forensic technique to detect frame insertion, deletion and duplication types of video forgery. In the first step, we detect outlier frames, based on Haralick coded frame correlation; and in the second step, we perform a finer degree of detection, to eliminate false positives, hence to optimize the forgery detection accuracy. Our experimental results prove that the proposed method outperforms the state–of–the–art with an average F1 score of 0.97 in terms of inter–frame video forgery detection accuracy.

Video authentication using spatio temporal relationship for tampering detection

This paper discusses a novel approach to detect inter frame and intra frame video forgery using content based signature. A novel technique called the “Spatio Temporal Triad Feature Relationship” (STTFR) is employed to generate a unique content based signature – value for any given video sequence. The proposed STTFR algorithm aims to verify video integrity through the creation of a 128 bit message digest from the input video of variable length that will be unique to that video and acts as a fingerprint. Change in the video sequence, either at the spatial or at the temporal level will result in a different fingerprint than the one obtained originally. The knowledge of the signature will not enable any person/entity to recreate the original video as the signature is generated by combining spatial and temporal fingerprints in an orderly and systematic approach. We have verified our technique with standard datasets and found accurate results.

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.

Bayesian inference by reversible jump MCMC for clustering based on finite generalized inverted Dirichlet mixtures

The goal of constructing models from examples has been approached from different perspectives. Statistical methods have been widely used and proved effective in generating accurate models. Finite Gaussian mixture models have been widely used to describe a wide variety of random phenomena and have played a prominent role in many attempts to develop expressive statistical models in machine learning. However, their effectiveness is limited to applications where underlying modeling assumptions (e.g., the per-components densities are Gaussian) are reasonably satisfied. Thus, much research efforts have been devoted to developing better alternatives. In this paper, we focus on constructing statistical models from positive vectors (i.e., vectors whose elements are strictly greater than zero) for which the generalized inverted Dirichlet (GID) mixture has been shown to be a flexible and powerful parametric framework. In particular, we propose a Bayesian density estimation method based upon mixtures of GIDs. The consideration of Bayesian learning is interesting in several respects. It allows to take uncertainty into account by introducing prior information about the parameters, it allows simultaneous parameters estimation and model selection, and it allows to overcome learning problems related to over- or under-fitting. Indeed, we develop a reversible jump Markov Chain Monte Carlo sampler for GID mixtures that we apply for simultaneous clustering and feature selection in the context of some challenging real-world applications concerning scene classification, action recognition, and video forgery detection.

VIDEO FOREGERY DEDUCTION OF INTERFRAME DUPLICATION

Video forgery detection aims to distinguish video forgeries from original videos. Video forgery detection in the form of features extraction from clustering the frames and matched with original videos. Scale Invariant Feature Transform (SIFT) are improved for detection of copy move attacks. Image keypoints are extracted and multi-dimensional feature vector named as SIFT descriptor is generated for each keypoint. Then, these keypoints are matched using distance among their descriptors. The experimental results show that our proposed algorithm has good at detection of copy move attacks. Total percentage of forged identify which frame to be forged and designed application as window based application with image processing techniques.

Image forensics using color illumination, block and key point based approach

Every individual is keen to exhibit socialism and connectedness posting their personal photos and videos on several social websites. Thus, it has become literally easy for the onlookers to see and modify their photos and videos. Here the concept enumerates in picture as image forensics, whereby it is possible to examine the authenticity and genuineness of photograph and video into consideration. In addition to this, nowadays photograph and videos are considered as a firm and valid proof in the court room for investigation, validation and judgement. Several experts are continuously working in an image forensic field to discover and develop better techniques for the detection of forgeries in image and videos. Detection of image forgeries is done in two ways. Firstly the forged image we are already familiar with is called active forgery detection technique and secondly, where we don’t know the forgery, then is referred to as the passive forgery detection technique. Passive technique is incorporated to detect forgery in this paper where hybridization is used. We have used DWT, color illumination Algorithm, SLIC Algorithm; SIFT Algorithm, Correlation Coefficient Map generation Algorithm, Block Matching Threshold Algorithm and Feature Extraction Algorithm for the detection and ramifying forgeries. The novelty of the proposed hybrid technique is the use of color illumination which detect image edges and trace them correctly to detect forged region. We have tested 48 images from database and find out image forgery detection at image level with Precision = 97. 25%; Recall = 100% and F1 = 98. 53%.

A Fast Forgery Detection Algorithm Based on Exponential-Fourier Moments for Video Region Duplication

Region duplication is one of the most common methods of video forgery. Existing forgery detection algorithms generally suffer from inefficiency and are not effective for the forged regions with mirroring. To address these problems, we present a fast forgery detection algorithm based on Exponential-Fourier moments (EFMs) for detecting region duplication in videos. The algorithm first extracts EFMs features from each block in the current frame, and performs a fast match to find potential matching pairs. Then, a postverification scheme is designed to eliminate falsely matched pairs and locate the altered regions in the current frame. Finally, an adaptive parameter-based fast compression tracking algorithm is used to track the tampered regions in the subsequent frames. The experimental results show that our proposed algorithm has higher detection accuracy and computational efficiency than those of previous algorithms.

Deep Learning for Detection of Object-Based Forgery in Advanced Video

Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN) to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.