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Applied Sciences
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New Development in Machine Learning in Image and Video Forensics
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New Development in Machine Learning in Image and Video Forensics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 290

Special Issue Editors

Dr. Ngai Fong Bonnie Law

E-Mail Website
Guest Editor
Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: genomics; wavelets; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Yunxia Liu

E-Mail Website
Guest Editor
School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Interests: ship detection; SAR

Special Issue Information

Dear Colleagues,

Recent developments in machine learning have significantly advanced the capabilities of image and video forensics. This Special Issue focuses on novel algorithms, architectures, and frameworks that enhance source identification, tampering detection, and content authentication in digital media.

Submissions are invited on topics including, but not limited to, deep learning-based methods for source camera identification and camera device linking, and image and video forgery detection. High relevance will be given to methods addressing challenges in real-world conditions, such as forensic analysis in image/video sourced from applications or social media platforms, surgical videos, or high-resolution surveillance systems.

Studies offering new forensic datasets, interpretable models, or efficient authentication schemes for resource-constrained environments are also encouraged. Research combining theoretical innovation with practical deployment potential, such as in law enforcement, healthcare, or security applications, is of particular interest.

The aim of this Special Issue is to provide a platform for sharing cutting-edge machine learning approaches in multimedia forensics. Contributions that present robust, scalable, and explainable solutions for forensic trace extraction and evidence analysis will help shape future directions in the field. Original research, review articles, and application-driven studies are all welcome for consideration.

Dr. Ngai Fong Bonnie Law
Dr. Yunxia Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning forensics
  • image tampering detection
  • source camera identification
  • deep learning for multimedia
  • video authentication
  • forensic neural networks
  • explainable AI in forensics

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Published Papers (1 paper)

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Research

27 pages, 2292 KB  
Article
Source Camera Identification via Explicit Content–Fingerprint Decoupling with a Dual-Branch Deep Learning Framework
by Zijuan Han, Yang Yang, Jiaxuan Lu, Jian Sun, Yunxia Liu and Ngai-Fong Bonnie Law
Appl. Sci. 2026, 16(3), 1245; https://doi.org/10.3390/app16031245 - 26 Jan 2026
Viewed by 133
Abstract
In this paper, we propose a source camera identification method based on disentangled feature modeling, aiming to achieve robust extraction of camera fingerprint features under complex imaging and post-processing conditions. To address the severe coupling between image content and camera fingerprint features in [...] Read more.
In this paper, we propose a source camera identification method based on disentangled feature modeling, aiming to achieve robust extraction of camera fingerprint features under complex imaging and post-processing conditions. To address the severe coupling between image content and camera fingerprint features in existing methods, which makes content interference difficult to suppress, we develop a dual-branch deep learning framework guided by imaging physics. By introducing physical consistency constraints, the proposed framework explicitly separates image content representations from device-related fingerprint features in the feature space, thereby enhancing the stability and robustness of source camera identification. The proposed method adopts two parallel branches: a content modeling branch and a fingerprint feature extraction branch. The content branch is built upon an improved U-Net architecture to reconstruct scene and color information, and further incorporates texture refinement and multi-scale feature fusion to reduce residual content interference in fingerprint modeling. The fingerprint branch employs ResNet-50 as the backbone network to learn discriminative global features associated with the camera imaging pipeline. Based on these branches, fingerprint information dominated by sensor noise is explicitly extracted by computing the residual between the input image and the reconstructed content, and is further encoded through noise analysis and feature fusion for joint camera model classification. Experimental results on multiple public-source camera forensics datasets demonstrate that the proposed method achieves stable and competitive identification performance in same-brand camera discrimination, complex imaging conditions, and post-processing scenarios, validating the effectiveness of the proposed disentangled modeling and physical consistency constraint strategy for source camera identification. Full article
(This article belongs to the Special Issue New Development in Machine Learning in Image and Video Forensics)
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