Forensic detection of image manipulation using statistical intrinsic fingerprints

By Matthew Christopher Stamm, K.J.R. Liu. IEEE Transactions on Information Forensics and Security, 2010

359 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/04f45db0a9d02633a56f606baf88389ca0fbcdf3

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection (2017, background): “A number of papers have been proposed to detect one or the other of such elaborations [13, 15, 16, 22, 25].”
Digital image forgery detection using passive techniques: A survey (2013, methodology): “Stamm and Liu (2008, 2010) presented a method to detect globally and locally applied contrast enhancement and the use of histogram equalization by searching for the identifying features of each operations intrinsic fingerprint.”
Digital Image Authentication From JPEG Headers (2011, background): “…[5], [6]; color filter array artifacts [7], [8]; inconsistencies in camera response function [9]; inconsistencies in lighting and shadows [10]–[13]; inconsistencies in chromatic aberrations [14], [15]; inconsistencies in sensor noise [16], [17]; and inconsistencies in statistical features [18].”
A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer (2016, methodology): “This approach has been used to develop algorithms targeted at detecting image manipulations such as resizing and resampling [17, 8], median filtering [9, 7], contrast enhancement [19], etc.”
Digital image forensics: a booklet for beginners (2010, background): “A more complete treatment of image processing for malicious tampering is discussed in [85].”
Constrained Convolutional Neural Networks: A New Approach Towards General Purpose Image Manipulation Detection (2018, methodology): “This approach has been successful in detecting many types of image tampering such as resizing and resampling [2]–[6], median filtering [7]–[10], contrast enhancement [11]–[14], multiple JPEG compression [15]–[18], etc.”
Information Forensics: An Overview of the First Decade (2013, methodology): “Unlike techniques that identify copy-move forgeries through block matching, SIFT-based approaches can identify duplicate image regions even if the duplicated region has been subject to scaling, rotation, an affine transform, or illumination adjustment [31], [32].”
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection (2020, background): “As a result, the area of research traditionally dedicated to general image and video fake detection [5], is being invigorated and is now dedicating growing efforts for detecting facial manipulation in image and video [6], [7].”
Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition (2014, neutral): “In the current state-of-the-art, the rationale behind the use of IQA features for liveness detection is supported by three factors: • Image quality has been successfully used in previous works for image manipulation detection [24], [25] and steganalysis [26], [27] in the forensic ﬁeld.”