Detecting Video Forgeries Based on Noise Characteristics

Kobayashi, Michihiro; Okabe, T.; Sato, Yoichi

doi:10.1007/978-3-540-92957-4_27

Cited by 40 publications

(16 citation statements)

References 14 publications

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“…Mean of intensities of each sub block is divided by mean of respective frame one by one as shown in (2). Hence for four different sub blocks of a particular frame, four new features are derived.…”

Section: Ratio For Each Sub Block: This Will Results In Four Identicalmentioning

confidence: 99%

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Detection of Frame Duplication Type of Forgery in Digital Video Using Sub-block Based Features

Singh

Pant

Tripathi

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. With the easy availability and operability of video editing tools, any video could be edited in short span of time. Sometimes, these modifications change the actual meaning of targeted video. Hence, before making any judgment and opinion about such multimedia contents, it is necessary to verify their genuineness. A video can be tampered by various different attempts. Each different attempt derives a new type of forgery in videos. Among various types of attack on video, frame duplication is a common type of attack. Frames are duplicated and pasted into same video in order to either hide or add false information. We propose Sub Blocked based features to detect frame duplication. The experimental results show higher accuracy that not only detects but also localize duplicated frames as well.

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Section: Ratio For Each Sub Block: This Will Results In Four Identicalmentioning

confidence: 99%

“…Kobayashi et al [2] proposed a powerful method to detect a duplicated region in the video based on noise characteristics. With the help of inherent parameters of a camera, result found was more accurate and reliable.…”

Section: Introductionmentioning

confidence: 99%

Detection of Frame Duplication Type of Forgery in Digital Video Using Sub-block Based Features

Singh

Pant

Tripathi

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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“…Passive approaches use internal features to detect if a video has been tampered in some ways. Some methods propose to use noise characteristics [5] to detect possible forgeries. Other works try to detect proofs of the evidence of a double compression [6,7].…”

Section: State Of the Artmentioning

confidence: 99%

A Tool to Support the Creation of Datasets of Tampered Videos

Ardizzone

Mazzola

2015

Image Analysis and Processing — ICIAP 2015

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Abstract. Digital Video Forensics is getting a growing interest from the Multimedia research community, as the need for methods to validate the authenticity of a video content is increasing with the number of videos freely available to the digital users. Unlike Digital Image Forensics, to our knowledge, there are not standard datasets to test video forgery detection techniques. In this paper we present a new tool to support the users in creating datasets of tampered videos. We furthermore present our own dataset and we discuss some remarks about how to create forgeries difficult to be detected by an observer, to the naked eye.

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“…In [20], the authors proposed another method that addressed the observation that a forged video will be recompressed, and the resulting artifacts due to the double quantization of coefficients can be used to detect forgery. In reference [5], the authors suggested a detection method for forged regions based on the inconsistencies of noise characteristics caused by the forged areas in different videos. A method based on accumulated differential images was proposed in [2], which used textural features to detect the forgery of moving objects removed from a static background.…”

Section: Introductionmentioning

confidence: 99%

Using similarity analysis to detect frame duplication forgery in videos

Yang

Huang

2014

Multimed Tools Appl

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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 similarityanalysis-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.

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Detecting Video Forgeries Based on Noise Characteristics

Cited by 40 publications

References 14 publications

Detection of Frame Duplication Type of Forgery in Digital Video Using Sub-block Based Features

Detection of Frame Duplication Type of Forgery in Digital Video Using Sub-block Based Features

A Tool to Support the Creation of Datasets of Tampered Videos

Using similarity analysis to detect frame duplication forgery in videos

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