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Video Inter-frame Forgery Identification by a Statistical Method
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Nowadays, due to the increasing crime and theft around the world, surveillance security systems play an important role. On the other hand, the availability of video editing tools has made authenticity of video contents significant and urgent mission to use as strong evidence in the courts. Frame duplication with/without shuffling is a common form of video forgery to repeat or cover-up an event in a video’s scene. In this paper, we propose a robust method to detect inter-frame duplication forgery using a temporal average of each shot and statistical textural features. Duplicated shots containing frames that are reordered during the forgery process (frame shuffling), cannot be classified as tampered shots by the existing methods leading to an increase in false positives. To address this issue, we use a temporal average of each shot which found to be invariant with different orders. Our method is capable of detecting duplicate shots that do not have any tracing points (discontinuity points). Experimental results show that our method has achieved improved accuracy on frame duplication detection with lower computational time. Furthermore, it has successfully detected frame shuffling with high accuracy rates, even when the forged video has undergone post-processing operations such as Gaussian blurring, noise addition, brightness modification, and compression.
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.
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.
Forgery involving region duplication is one of the most common types of video tampering. However, few algorithms have been suggested for detecting this type of forgery effectively, especially for videos to which a mirroring operation was applied. In this paper, we summarize the properties of duplication forgery of video regions and propose a novel algorithm to detect this forgery. First, the algorithm extracts the feature points in the current frame. The tampered areas in the current frame are then searched, which is implemented in three steps. Finally, our algorithm detects the tampered areas in the remaining frames using spatio-temporal context learning and outputs the detection results. The experimental results demonstrate the satisfactory performance of our algorithm for detecting videos subjected to mirror operations and its higher efficiency than previous algorithms.
Detection of frame deletion is of great significance in the field of video forensics. Several approaches have been presented through analyzing the side effect caused by frame deletion. However, most of the current approaches can detect the existence of frame deletion but not the exact location of it. In this paper, we present a method which can directly locate the frame deletion point. Through the analysis of the distinguishing fluctuation feature of motion residual caused by frame deletion compared to interference frames and ordinary video content jitter in tampered video sequence, an algorithm based on the total motion residual of video frame is proposed to detect the frame deletion point. Moreover, an initiative processing procedure for frame motion residual and an adaptive threshold detector are introduced so that the robustness of the detection can be markedly improved. Experimental results show that the proposed algorithm is effective in generalized scenarios such as different encoding settings, rapid or slow motion sequences and multiple group of picture deletion. It also has a high performance that the true positive rate reaches 90% and the false alarm rate is less than 0.8%.
A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary filters need not be orientation-dependent. Thus, intensity changes at a given scale are best detected by finding the zero values of delta 2G(x,y)*I(x,y) for image I, where G(x,y) is a two-dimensional Gaussian distribution and delta 2 is the Laplacian. The intensity changes thus discovered in each of the channels are then represented by oriented primitives called zero-crossing segments, and evidence is given that this representation is complete. (2) Intensity changes in images arise from surface discontinuities or from reflectance or illumination boundaries, and these all have the property that they are spatially. Because of this, the zero-crossing segments from the different channels are not independent, and rules are deduced for combining them into a description of the image. This description is called the raw primal sketch. The theory explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround delta 2G filters acting on the image forms the basis for a physiological model of simple cells (see Marr & Ullman 1979).
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.
Video tampering is a process of malicious alteration of video content, so as to conceal an object, an event or change the meaning conveyed by the imagery in the video. Fast proliferation of video acquisition devices and powerful video editing software tools have made video tampering an easy task. Hence, the authentication of video files (especially in surveillance applications like bank ATM videos, medical field and legal concerns) are becoming important. Video tampering detection aims to find the traces of tampering and thereby evaluate the authenticity and integrity of the video file. These methods can be classified into active and passive (blind) methods. In this paper, we present a survey on passive video tampering detection methods. Passive video tampering detection methods are classified into the following three categories based on the type of forgery they address: Detection of double or multiple compressed videos, Region tampering detection and Video inter-frame forgery detection. At first, we briefly present the preliminaries of video files required for understanding video tampering forgery. The existing papers surveyed are presented concisely; the features used and their limitations are summarized in a compact tabular form. Finally, we have identified some open issues that help to identify new research areas in passive video tampering detection.
Duplication of selected frames from a video to another location in the same video is one of the most common methods of video forgery. However, few algorithms have been suggested for detecting this tampering operation. This paper proposes an effective similarity-analysis-based method for frame duplication detection that is implemented in two stages. In the first stage, the features of each frame are obtained via SVD (Singular Value Decomposition). Next, the Euclidean distance is calculated between features of each frame and the reference frame. After dividing the video sequence into overlapping sub-sequences, the similarities between the sub-sequences are calculated, and then our algorithm identifies those video sequences with high similarity as candidate duplications. In the second stage, the candidate duplications are confirmed through random block matching. The experimental results show that our algorithm provides detection accuracy that is higher than the previous algorithms, and it has an outstanding performance in terms of time efficiency.
A recent case of scientific fraud, involving manipulated im- ages in a high-profile scientific publication, has sent shock- waves through the scientific community. By some measures, however, this case is not isolated - in at least one journal, it is estimated that as many as 20% of accepted manuscripts contain figures with inappropriate manipulations, and 1% with fraudulent manipulations. Several scientific editors are considering putting safeguards in place to help reduce these numbers. While sensible policy and awareness are certainly important, there is likely to be a need for computational techniques that automatically detect common forms of tam- pering. We describe three such techniques for detecting traces of tampering in scientific images. Specifically, im- age segmentation techniques are employed to detect image deletion, "healing", and duplication.
Contenido: Introducción; La imagen digitalizada y sus propiedades; Estructuras de datos para el análisis de imágenes; Preprocesamiento de imágenes; Segmentación; Representación y descripción de la forma; Reconocimiento del objeto; Tercera dimensión, geometría y radiometría; Uso de la tercera dimensión; Morfología matemática; Compresión de imagen; Textura; Análisis del movimiento.
We introduce a novel statistical procedure for clustering categorical data based on Hamming distance (HD) vectors. The proposed method is conceptually simple and computationally straightforward, because it does not require any specific statistical models or any convergence criteria. Moreover, unlike most currently existing algorithms that compute the class membership or membership probability for every data point at each iteration, our algorithm sequentially extracts clusters from the given dataset. That is, at each iteration our algorithm strives to identify only one cluster, which will then be deleted from the dataset at the next iteration; this procedure repeats until there are no more significant clusters in the remaining data. Consequently, the number of clusters can be determined automatically by the algorithm. As for the identification and extraction of a cluster, we first locate the cluster center by using a Pearson chi-squared-type statistic on the basis of HD vectors. The partition of the dataset produced by our algorithm is unique and insensitive to the input order of data points. The performance of the proposed algorithm is examined using both simulated and real world datasets. Comparisons with two well-known clustering algorithms, K-modes and AutoClass, show that the proposed algorithm substantially outperforms these competitors, with the classification rate or the information gain typically improved by several orders of magnitude. Computational complexity and run time comparisons are also provided.
Copy Create Video Forgery Detection Techniques Using Frame Correlation Difference by Referring SVM Classifier
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- S Murali
- B S Anami
Detecting Video Inter-Frame Forgeries Based on Convolutional Neural Network Model
- X H Nguyen
- Y Hu
- M A Amin
- K G Hayat
Introduction to Neural Networks using MATLAB 6.0
- S N Deepa
- S N Sivanandam
- S Sumathi
Copy Create Video Forgery Detection Techniques Using Frame Correlation Difference by Referring SVM Classifier
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