Digital image forensics via intrinsic fingerprints

By Ashwin Swaminathan, Min Wu, K.J.R. Liu. IEEE Transactions on Information Forensics and Security, 2008

320 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/34f4de8b1b2277c5e003af5fc8d497db26fc8269

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In Computer Vision and Pattern Recognition

Citations in the corpus

Fighting Fake News: Image Splice Detection via Learned Self-Consistency (2018, methodology): “We thank Hany Farid and Shruti Agarwal for their advice, assistance, and inspiration in building this project, David Fouhey, Saurabh Gupta, and Allan Jabri for helping with the editing, Peng Zhou for helping with experiments, and Abhinav Gupta for letting us use his GPUs.”
A Deep Journey into Super-resolution (2019, background): “…has important applications in many other domains e.g. object detection in scenes [6] (particularly small objects [7]), face recognition in surveillance videos [8], medical imaging [9], improving interpretation of images in remote sensing [10], astronomical images [11], and forensics [12].”
Densely Residual Laplacian Super-Resolution (2019, background): “…being a difﬁcult problem, it is useful in many computer vision applications such as surveillance imaging [1], medical imaging [2], forensics [3], object classiﬁcation [4] etc. Deep convolutional neural network (CNN) super-resolution methods [7], [11], [12] have shown improvement over…”
A Survey on Metaverse: Fundamentals, Security, and Privacy (2022, background): “However, truthful participation also violates user’s privacy, e.g., the true bid in auctions may reveal user’s true valuation on the items.”
Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts (2012, unknown): “As to the ﬁrst class, Swaminathan et al. in [8] propose a method for camera identiﬁcation by the estimation of the CFA pattern and interpolation kernel; while in [9] the same authors exploit the inconsistencies among the estimated demosaicing parameters as proof of tampering.”
Media Forensics and DeepFakes: An Overview (2020, methodology): “Departing from this “agnostic” approach, the method proposed in [47] uses the noise residual to estimate the imaging model and define an intrinsic camera fingerprint.”
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, background): “As a result, the area of research traditionally dedicated to general media forensics [5]–”
Noiseprint: A CNN-Based Camera Model Fingerprint (2018, background): “[42], [43] are conceptually related to our noiseprint proposal since they aim at identifying all the possible traces of in-camera and out-camera processing, called intrinsic fingerprints.”
Splicebuster: A new blind image splicing detector (2015, background): “Several effective algorithms are based on this simple idea, like [4] and Fig.”
Information Forensics: An Overview of the First Decade (2013, methodology): “This blind-deconvolution approach can also be used to perform universal steganalysis to determine whether an expected camera output image has additional data hidden in it, and can detect both quantization based and spread-spectrum based steganography [100].”
Region Duplication Detection Using Image Feature Matching (2010, background): “Other intrinsic cues from the imaging process, including color filter array interpolation patterns [44], [46], camera response functions [30], and camera sensor noise [9], [18], or general statistical properties of untampered natural images [34],”