A part-level learning strategy for JPEG image recompression detection

Taimori, Ali; Razzazi, Farbod; Behrad, Alireza; Ahmadi, Ali; Babaie‐Zadeh, Massoud

doi:10.1007/s11042-020-10200-4

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The authors of [93] suggested a part-level middle-out learning technique for structurally efficient classification to detect dual compression. The authors first show that single and double compressed data with different JPEG coder settings will form a limited number of coherent clusters in a featured space representation.…”

Section: Forensic Methods For Handling Image Filesmentioning

confidence: 99%

Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

et al. 2021

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In recent years, there has been a dramatic change in attitude towards computers and the use of computer resources in general. Cloud and Edge computing have emerged as the most widely used technologies, including fog computing and the Internet of Things (IoT). There are several benefits in exploiting Cloud and Edge computing paradigms, such as lower costs and higher efficiency. It provides data computation and storage where data are processed, enables better data control, faster understanding and actions, and continuous operation. However, though these benefits seem to be appealing, their effects on computer forensics are somewhat undesirable. The complexity of the Cloud and Edge environments and their key features present many technical challenges from multiple stakeholders. This paper seeks to establish an in-depth understanding of the impact of Cloud and Edge computing-based environmental factors. Software and hardware tools used in the digital forensic process, forensic methods for handling tampered sound files, hidden files, image files, or images with steganography, etc. The technical/legal challenges and the open design problems (such as distributed maintenance, multitasking and practicality) highlight the various challenges for the digital forensics process.

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Section: Forensic Methods For Handling Image Filesmentioning

confidence: 99%

Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

et al. 2021

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Block-Based Discrete Cosine Approaches for Removal of JPEG Compression Artifacts

Sandhu

2022

Emerging Technologies in Data Mining and Information Security

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Forensic discrimination between traditional and compressive imaging by blurring kernel investigation

Taimori,

Zayyani,

Marvasti

2023

Multimed Tools Appl

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Image forensics encompasses a set of scientific tests to investigation of a suspected event via intrinsic clues of imaging pipeline. Traditional image sensing at the Nyquist-Shannon rate as well as the new modality of compressive imaging below the rate are two main types of sensing in photography and imaging applications. Hence, for forensic investigators, it would importantly necessitate the ability to discriminate among images captured by them. However, due to the complex nonlinear nature of imaging processes, investigating imagers’ traces is a difficult task. To this intent, we first systematically model the imaging pipelines as an encoder-decoder pair. For exploring distinguishable traces, we mathematically simplify and linearize the pair for compressive imaging and two main forms of traditional image sensing with or without compression. Our theoretical analyses on the approximate linear models reveal blurring kernels of different imagers have discriminability. To validate it in real-world scenarios, we considered the whole imaging process as an inverse problem and estimated the blurring kernel based on a deconvolution approach, where the discriminability is also justified by information visualization. Then, we designed a pipeline classification system, where a deep convolutional neural network is trained by the estimated blurring kernels to be able to classify the three imaging systems. Our results in compressive imaging identification show an accuracy improvement about 3.7 % in comparison to the best result among compared methods. Implementation codes are available for research and development.

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A part-level learning strategy for JPEG image recompression detection

Cited by 5 publications

References 21 publications

Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

Cloud and Edge Computing-Based Computer Forensics: Challenges and Open Problems

Block-Based Discrete Cosine Approaches for Removal of JPEG Compression Artifacts

Forensic discrimination between traditional and compressive imaging by blurring kernel investigation

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