Statistical H.264 Double Compression Detection Method Based on DCT Coefficients

Mahfoudi, Gaël; Retraint, Florent; Morain-Nicolier, Frédéric; Pic, Marc

doi:10.1109/access.2022.3140588

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…Subsequently, transition probability matrices of the noise were used to detect doubly compressed video sequences. Mahfoudi et al demonstrated that DCT coefficients had a Laplacian distribution dependent on the quantization parameter used in the encoder [100], which could be used for double compression detection. Uddin et al investigated HEVC/H.265 [101] encoded videos and utilized both statistical and deep convolutional neural network features for multiple compression detection [102].…”

Section: Fingerprint-based Detectionmentioning

confidence: 99%

See 1 more Smart Citation

An Overview of Recent Work in Media Forensics: Methods and Threats

Bhagtani¹,

Yadav²,

Bartusiak³

et al. 2022

Preprint

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In this paper, we review recent work in media forensics for digital images, video, audio (specifically speech), and documents. For each data modality, we discuss synthesis and manipulation techniques that can be used to create and modify digital media. We then review technological advancements for detecting and quantifying such manipulations. Finally, we consider open issues and suggest directions for future research.

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Section: Fingerprint-based Detectionmentioning

confidence: 99%

“…The encoding parameters are also used extensively for double compression detection. In [100,102,199,204], the authors investigated double compression detection using encoding parameters in H.264 bitstreams. In [102,[205][206][207], the authors used encoding parameters in H.265 video bitstreams for double compression detection.…”

Section: Metadata Forensicsmentioning

confidence: 99%

An Overview of Recent Work in Media Forensics: Methods and Threats

Bhagtani¹,

Yadav²,

Bartusiak³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The key will be given out if the matching score is lower than the threshold. Bottom-hat filtering, a straightforward and precise method for mining principal lines from palm prints, was introduced in [12]. Normalization, median filtering, average filters along four prefixed directions like 0, 45, 90, 135; grayscale bottom hat filtering; a combination of bottom hat filtering, binarization, and post processing; and so on are all examples of bottom hat filtering.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Biometric Based Person Identification Using Machine Learning

N¹

2022

Journal of Pharmaceutical Negative Results

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When compared to the more traditional methods of authentication, biometric systems offer a much higher level of protection for a wide range of uses (like pin, passwords etc.). Various sectors of modern society can find use for biometric systems. Among these are authentication for computers, attendance tracking for businesses, financial transactions, safeguarding private information, securing access to buildings, and ensuring the safety of travellers at airports. Identifying and verifying individuals via their unique physical and behavioural characteristics is the primary function of the biometric system. Importance of biometric systems in modern society is analysed in this paper. Single-trait biometric user recognition is currently used, but it does not offer sufficient security for critical programmes. Multimodal biometric systems are used to get around these issues. Physical and behavioural characteristics, like fingerprints and DNA, signatures and fingerprints, etc., are all part of a multimodal biometric system's arsenal for authenticating users. In addition to the hard biometric features already mentioned (skin colour, age, height, hair colour, eye colour, gender, etc.), the use of a person's soft biometric traits is becoming increasingly common. While soft biometrics can be used to boost the efficiency of biometric systems that focus on other characteristics, they are not without their own set of drawbacks, such as a lack of permanence and distinctive behaviour. User authentication, security, and performance are all boosted by the work presented in this paper using Machine learning (F-SVM -Fuzzy-Support Vector Machine) model.

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“…Recently, Li [ 27 ] proposed a semi-supervised learning method to detect double-compressed video using Gaussian density-based one-class classifiers. Mahfoudi proposed the statistical H.264 double-compression detection method based on discrete cosine transform (DCT) coefficients [ 28 ]. A motion-adaptive algorithm is proposed to detect HEVC double compression with the same coding parameters [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Double-Compressed Videos Using Descriptors of Video Encoders

Lee

Byun

2022

Sensors

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In digital forensics, video becomes important evidence in an accident or a crime. However, video editing programs are easily available in the market, and even non-experts can delete or modify a section of an evidence video that contains adverse evidence. The tampered video is compressed again and stored. Therefore, detecting a double-compressed video is one of the important methods in the field of digital video tampering detection. In this paper, we present a new approach to detecting a double-compressed video using the proposed descriptors of video encoders. The implementation of real-time video encoders is so complex that manufacturers should develop hardware video encoders considering a trade-off between complexity and performance. According to our observation, hardware video encoders practically do not use all possible encoding modes defined in the video coding standard but only a subset of the encoding modes. The proposed method defines this subset of encoding modes as the descriptor of the video encoder. If a video is double-compressed, the descriptor of the double-compressed video is changed to the descriptor of the video encoder used for double-compression. Therefore, the proposed method detects the double-compressed video by checking whether the descriptor of the test video is changed or not. In our experiments, we show descriptors of various H.264 and High-Efficiency Video Coding (HEVC) video encoders and demonstrate that our proposed method successfully detects double-compressed videos in most cases.

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Statistical H.264 Double Compression Detection Method Based on DCT Coefficients

Cited by 8 publications

References 31 publications

An Overview of Recent Work in Media Forensics: Methods and Threats

An Overview of Recent Work in Media Forensics: Methods and Threats

Hybrid Biometric Based Person Identification Using Machine Learning

Detection of Double-Compressed Videos Using Descriptors of Video Encoders

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