Double compression detection in HEVC-coded video with the same coding parameters using picture partitioning information

Uddin, K. M. Shihab; Yang, Yoonmo; Oh, Byung Tae

doi:10.1016/j.image.2022.116638

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…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]. In [103], the authors used Scale-Invariant Feature Transform (SIFT) [104] features to reveal intra-frame copy-paste attacks by detecting edge lines.…”

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%

“…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. Altinisik et al used both encoding parameters and video container metadata for video device attribution, which resulted in improved performance compared to methods using container metadata only [208].…”

Section: Metadata Forensicsmentioning

confidence: 99%

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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%

Section: Metadata Forensicsmentioning

confidence: 99%

Section: Metadata Forensicsmentioning

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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“…Following enhancement, a volumetric bit rate The spatial relationship between close and distant blocks ensures a volumetric bit rate when it has been improved. For cost savings, using a method that adaptively nominates block and sub-block coding orders is advised [29]. In opposition to HEVC interfering with set blocking orders.…”

Section: Introductionmentioning

confidence: 99%

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Dommeti,

Dharani,

Shasidhar

et al. 2024

SCPE

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In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

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“…Second, based on the suggested LSR, they create an approach for optimizing video-compression in a way that simultaneously improves user perceptual quality, computer evaluation accuracy, as well as compression-efficiency. Finally, a high-mobility, low-complexity (HMFVC) codec is created, that outperforms both conventional video-codecs and learning video-compression methods in terms of motion recognition efficiency and reconstructive quality [36].…”

mentioning

confidence: 99%

Video perception enhancement using effective encoding optimization in future generation wireless network

Thulajanaik,

Manjanaik

2023

IJEECS

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<span>The cumulative effects of network transmission link imperfections and real-time limitations of video data result in multiple challenges for video transmission in massive millimeter wave (mmwave) network. The challenges, together with the increasing expectations of users for good-quality videos, further extend the complexity particularly in dense areas. To address the issues this work proposes perceptual quality aware video encoding (PQAVE) scheme in two phases: in phase 1 an effective perceptual quality encoding method leveraging low-rank approximation to reduce overall video size is done. In phase 2, a novel bitstream optimization technique is introduced to improve perceptual quality of videos. Experiment are conducted using standard video dataset show the proposed PQAVE model attain better bit error rate (BER), symbol error rate (SER), error vector magnitude (EVM), and coding gain in comparison with existing perceptual video encoding scheme.</span>

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Double compression detection in HEVC-coded video with the same coding parameters using picture partitioning information

Cited by 13 publications

References 19 publications

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

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

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

Video perception enhancement using effective encoding optimization in future generation wireless network

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