Downscaling Factor Estimation on Pre-JPEG Compressed Images

Liu, Xianjin; Lu, Wei; Zhang, Qin; Huang, Jiwu; Shi, Yun-Qing

doi:10.1109/tcsvt.2019.2893353

Cited by 42 publications

(10 citation statements)

References 28 publications

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“…In forensic video examination, it is usually noted that to maximize the use of storage space, many digital camera systems heavily compress video/images to reduce files size. This will cause in a loss of details that cannot be recovered and will introduce visible artefacts into the image [37].…”

Section: ) Compressionmentioning

confidence: 99%

Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation

Xiao

2019

IEEE Access

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As a result of the popularity of smart mobile devices and the low cost of surveillance systems, visual data are increasingly being used in digital forensic investigation. Digital videos have been widely used as key evidence sources in evidence identification, analysis, presentation, and report. The main goal of this paper is to develop advanced forensic video analysis techniques to assist the forensic investigation. We first propose a forensic video analysis framework that employs an efficient video/image enhancing algorithm for the low quality of footage analysis. An adaptive video enhancement algorithm based on contrast limited adaptive histogram equalization (CLAHE) is introduced to improve the closed-circuit television (CCTV) footage quality for the use of digital forensic investigation. To assist the video-based forensic analysis, a deeplearning-based object detection and tracking algorithm are proposed that can detect and identify potential suspects and tools from footages. INDEX TERMS Forensics investigation, forensic video analysis, video/image enhancement, object detection, deep learning.

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Section: ) Compressionmentioning

confidence: 99%

Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation

Xiao

2019

IEEE Access

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“…(2) The second category includes the techniques that seek the traces left by specific image manipulations such as resampling [8,9], contrast enhancement [10,11], median filtering [12], copy-move manipulations [13] and JPEG compression [14][15][16]. (3) The third category relies on the regularities or constraints that make images physically plausible.…”

Section: Introductionmentioning

confidence: 99%

PRNU-Based Content Forgery Localization Augmented With Image Segmentation

Lin

2020

IEEE Access

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“…A potential rise in multimedia tampering can seriously affect the security of our society. Therefore, multimedia information security [3]- [6] and multimedia forensics [7]- [10] have become important topics.…”

Section: Introductionmentioning

confidence: 99%

Image Deblocking Detection Based on a Convolutional Neural Network

Liu

et al. 2019

IEEE Access

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With the development of multimedia processing technology, it is becoming much easier to manipulate and tamper with digital video without leaving any visual clues. Because video compression is very common in digital videos, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces. Motion JPEG (MJPEG) is one of the most popular video formats, in which each video frame or interlaced field of a digital video sequence is compressed separately as a JPEG image. By splitting the MJPEG video into JPEG image frames, the tamper might employ powerful multimedia deblocking methods to cover up the video tampering traces. To the best our knowledge, there is no existing method for the forensics of deblocking. In this paper, we propose a novel method to detect deblocking, which can automatically learn feature representations based on a deep learning framework. We first train a supervised convolutional neural network (CNN) to learn the hierarchical features of deblocking operations with labeled patches from the training datasets. The first convolutional layer of the CNN serves as the preprocessing module to efficiently obtain the tampering artifacts. Then, we extract the features for an image with the CNN on the basis of a patch by applying a patch-sized sliding-window to scan the whole image. The generated image representation is then condensed by a simple feature fusion technique, i.e., regional pooling, to obtain the final discriminative feature. The experimental results on several public datasets demonstrate the superiority of the proposed scheme. INDEX TERMS Multimedia forensics, debocking detection, JPEG block artifacts, convolutional neural network.

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Downscaling Factor Estimation on Pre-JPEG Compressed Images

Cited by 42 publications

References 28 publications

Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation

Video-Based Evidence Analysis and Extraction in Digital Forensic Investigation

PRNU-Based Content Forgery Localization Augmented With Image Segmentation

Image Deblocking Detection Based on a Convolutional Neural Network

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