Warwick Image Forensics Dataset for Device Fingerprinting in Multimedia Forensics

Quan, Yijun; Li, Chang-Tsun; Zhou, Yujue; Li, Li

doi:10.1109/icme46284.2020.9102783

Cited by 15 publications

(5 citation statements)

References 21 publications

(29 reference statements)

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“…For evaluation of the proposed method, we used images from the VISION dataset [ 52 ], Warwick Image Forensic dataset [ 53 ], Daxing dataset [ 54 ], UNISA2020 [ 55 ], and a custom-built dataset. All datasets have different characteristics in terms of camera model and brand, image content, and number of devices of the same model.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Manisha

Lin

et al. 2022

Sensors

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Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

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Section: Experiments and Discussionmentioning

confidence: 99%

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Manisha

Lin

et al. 2022

Sensors

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“…In contemporary times, digital visual media has become one of the primary modes of communication. Digital images are the subject of numerous digital investigations, as some of them may contain illicit content [25]. This type of analysis seeks to uncover details about the image's origin, including its location and the individuals depicted in it.…”

Section: Computer Forensics Domainsmentioning

confidence: 99%

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

AlKhanafseh,

Surakhi

2024

Preprint

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The adoption of technology and digital tools across various user groups has brought about a heightened risk of cyber-attacks targeting devices and shared data. These elevated levels of cybercrime necessitate thorough investigations to address victims’ inquiries about the nature, causes, affected data, and impacted resources of the attack. Digital forensics serves as a crucial means to answer these queries. Additionally, it enables the formulation of strategies and delineation of roles essential for fortifying future defenses against such attacks. Within digital forensics, digital evidence holds pivotal importance in the investigative process as it serves as a cornerstone in legal proceedings to identify perpetrators. Numerous research endeavors focus on devising solutions to safeguard evidence, including centralized storage centers or cloud computing. These efforts aim to create frameworks integrating diverse security levels for sharing and preserving evidence effectively. In this paper, a new framework is proposed that seeks to amalgamate novel technologies with forensic practices to address challenges in the preservation phase of digital forensics. Strategies include segmenting evidence into smaller components, leveraging steganography to support preservation, and employing blockchain technology to ensure the integrity of collected evidence.

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“…Digital visual media nowadays is one of the principal means of communication. Digital images are the target of many digital investigations because some are contraband [98], [99]. This type of analysis looks for information about where the picture was taken and who is in the picture.…”

Section: G Multimedia Forensicsmentioning

confidence: 99%

A Comprehensive Survey on Computer Forensics: State-of-the-Art, Tools, Techniques, Challenges, and Future Directions

et al. 2022

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With the alarmingly increasing rate of cybercrimes worldwide, there is a dire need to combat cybercrimes timely and effectively. Cyberattacks on computing machines leave certain artifacts on target device storage that can reveal the identity and behavior of cyber-criminals if processed and analyzed intelligently. Forensic agencies and law enforcement departments use several digital forensic toolkits, both commercial and open-source, to examine digital evidence. The proposed research survey focuses on identifying the current state-of-the-art digital forensics concepts in existing research, sheds light on research gaps, presents a detailed introduction of different computer forensic domains and forensic toolkits used for computer forensics in the current era. The proposed survey also presents a comparative analysis based on the tool's characteristics to facilitate investigators in tool selection during the forensics process. Finally, the proposed survey identifies and derives current challenges and future research directions in computer forensics.

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Warwick Image Forensics Dataset for Device Fingerprinting in Multimedia Forensics

Abstract: Please refer to published version for the most recent bibliographic citation information. If a published version is known of, the repository item page linked to above, will contain details on accessing it.

Cited by 15 publications

References 21 publications

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

A New Evidence Preservation Forensics Model Using Blockchain and Stenography Techniques

A Comprehensive Survey on Computer Forensics: State-of-the-Art, Tools, Techniques, Challenges, and Future Directions

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