Information Forensics: An Overview of the First Decade
Open accessAbstract
In recent decades, we have witnessed the evolution of information technologies from the development of VLSI technologies, to communication and networking infrastructure, to the standardization of multimedia compression and coding schemes, to effective multimedia content search and retrieval. As a result, multimedia devices and digital content have become ubiquitous. This path of technological evolution has naturally led to a critical issue that must be addressed next, namely, to ensure that content, devices, and intellectual property are being used by authorized users for legitimate purposes, and to be able to forensically prove with high confidence when otherwise. When security is compromised, intellectual rights are violated, or authenticity is forged, forensic methodologies and tools are employed to reconstruct what has happened to digital content in order to answer who has done what, when, where, and how. The goal of this paper is to provide an overview on what has been done over the last decade in the new and emerging field of information forensics regarding theories, methodologies, state-of-the-art techniques, major applications, and to provide an outlook of the future.
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- MIN WU (S'95-M'01-SM'06-F'11) received the B.E. degree in electrical engineering and the B.A. degree in economics from Tsinghua University, Beijing, China (both with the highest honors), in 1996, and the Ph.D. degree in electrical engi- neering from Princeton University, Princeton, NJ, USA, in 2001. Since 2001, she has been with University of Maryland, College Park, MD, USA, where she is currently a Professor and a Univer- sity Distinguished Scholar-Teacher. She leads the Media and Security Team at the University of Maryland. Her current research interests include information security and forensics and multimedia signal processing. She has co-authored two books and holds eight U.S. patents on multimedia security and communications. Dr. Wu is a co-recipient of two Best Paper Awards from the IEEE Sig- nal Processing Society and EURASIP, respectively. She received a NSF CAREER Award in 2002, a TR100 Young Innovator Award from the MIT Technology Review Magazine in 2004, an ONR Young Investigator Award in 2005, a Computer World ''40 Under 40'' IT Innovator Award in 2007, an IEEE Mac Van Valkenburg Early Career Teaching Award in 2009, and a Uni- versity of Maryland Invention of the Year Award in 2012. She has served as Vice President-Finance of the IEEE Signal Processing Society from 2010 to 2012 and Chair of the IEEE Technical Committee on Information Forensics and Security from 2012 to 2013. She is an IEEE fellow for contributions to multimedia security and forensics.
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Bayar, B., & Stamm, M. C. (2017) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2017.7952537 · Cited by 72 · see citing works
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A Classification Engine for Image Ballistics of Social Data
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Learned Forensic Source Similarity for Unknown Camera Models
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Self-citationCamera model identification based on the generalized noise model in natural images
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Exposing Fake Images With Forensic Similarity Graphs
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Self-citationAdversarial Multimedia Forensics: Overview and Challenges Ahead
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Self-citationEl‐Shafai, W., Fouda, M. A., El‐Rabaie, E. M., & Abdel-Salam, N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15609-1 · Cited by 72 · see citing works
Robust forgery detection for compressed images using CNN supervision
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First Quantization Matrix Estimation From Double Compressed JPEG Images
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Joint image splicing detection in DCT and Contourlet transform domain
Zhang, Q., Lu, W., & Weng, J. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.07.013 · Cited by 59 · see citing works
PulseEdit: Editing Physiological Signals in Facial Videos for Privacy Protection
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Self-citationRevealing the Trace of High-Quality JPEG Compression Through Quantization Noise Analysis
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A Content Management Perspective on Fact-Checking
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On the vulnerability of deep learning to adversarial attacks for camera model identification
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Towards Open Set Camera Model Identification Using a Deep Learning Framework
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Self-citationStatistical Model of JPEG Noises and Its Application in Quantization Step Estimation
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A Deep Learning Approach in the Discrete Cosine Transform Domain to Median Filtering Forensics
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DCT-domain deep convolutional neural networks for multiple JPEG compression classification
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Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks
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Augmented convolutional feature maps for robust CNN-based camera model identification
Bayar, B., & Stamm, M. C. (2017) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2017.8297053 · Cited by 50 · see citing works
Self-citationCamera Identification Based on Domain Knowledge-Driven Deep Multi-Task Learning
Ding, X., Chen, Y., Tang, Z., & Huang, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2897360 · Cited by 50 · see citing works
Forensic source identification using JPEG image headers: The case of smartphones
Mullan, P., Rieß, C., & Freiling, F. (2019) · Digital Investigation · doi:10.1016/j.diin.2019.01.016 · Cited by 49 · see citing works
Statistical Detection of JPEG Traces in Digital Images in Uncompressed Formats
Pasquini, C., Boato, G., & Pérez‐González, F. (2017) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2017.2725201 · Cited by 47 · see citing works
Yao, Y., Hu, W., Zhang, W., Wu, T., & Shi, Y.-Q. (2018) · Sensors · doi:10.3390/s18041296 · Cited by 47 · see citing works
Computer Graphics Identification Combining Convolutional and Recurrent Neural Networks
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The Video Authentication and Camera Identification Database: A New Database for Video Forensics
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Self-citationImage Manipulation on Facebook for Forensics Evidence
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A Video Camera Model Identification System Using Deep Learning and Fusion
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Self-citationAdaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications
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Splicing Detection and Localization In Satellite Imagery Using Conditional GANs
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Self-citationA New Dataset for Source Identification of High Dynamic Range Images
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Morphological Filter Detector for Image Forensics Applications
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Application of DenseNet in Camera Model Identification and Post-processing DetectionPreprint
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An Analysis of Digital Forensics in Cyber Security
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Multimedia Forensics: discovering the history of multimedia contents
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Anti-Forensics and Countermeasures of Electrical Network Frequency Analysis
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Self-citationForensically determining the order of signal processing operations
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Self-citationMobile Phone Clustering From Speech Recordings Using Deep Representation and Spectral Clustering
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Analyzing and Detecting Digital Counterfeit Images using DenseNet, ResNet and CNN
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Improving PRNU Compression Through Preprocessing, Quantization, and Coding
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Reliability Map Estimation for CNN-Based Camera Model Attribution
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- Authors
- Stamm, M. C., Wu, M., & Liu, K. J. R.
- Affiliations
- University of Maryland, College Park (US)
- Venue
- IEEE Access
- Published
- 2013
- Volume
- 1
- Pages
- 167-200
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2046686077
- Cited by
- 394
- References
- 179
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 20 · 2023: 26 · 2022: 34 · 2021: 36 · 2020: 48 · 2019: 40 · 2018: 54 · 2017: 38 · 2016: 31 · 2015: 23 · 2014: 11 · 2013: 2 · 2012: 1
- FWCI
- 25.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Information Systems › Digital and Cyber Forensics
- Keywords
- information forensics, digital forensics, multimedia forensics, intellectual property protection, digital watermarking, digital signatures
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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