Machine Learning
Vignette and Exposure Calibration and Compensation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Can we trust digital image forensics?
Proceedings of the 15th international conference on Multimedia
Printer profiling for forensics and ballistics
Proceedings of the 10th ACM workshop on Multimedia and security
Scanner identification using feature-based processing and analysis
IEEE Transactions on Information Forensics and Security
Source camera identification using enhanced sensor pattern noise
IEEE Transactions on Information Forensics and Security
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Vision of the unseen: Current trends and challenges in digital image and video forensics
ACM Computing Surveys (CSUR)
Exposing digital forgeries in color filter array interpolated images
IEEE Transactions on Signal Processing - Part II
Digital camera identification from sensor pattern noise
IEEE Transactions on Information Forensics and Security
Digital Single Lens Reflex Camera Identification From Traces of Sensor Dust
IEEE Transactions on Information Forensics and Security
Determining Image Origin and Integrity Using Sensor Noise
IEEE Transactions on Information Forensics and Security
Defending Against Fingerprint-Copy Attack in Sensor-Based Camera Identification
IEEE Transactions on Information Forensics and Security
Ballistics Projectile Image Analysis for Firearm Identification
IEEE Transactions on Image Processing
An analysis on attacker actions in fingerprint-copy attack in source camera identification
WIFS '11 Proceedings of the 2011 IEEE International Workshop on Information Forensics and Security
Open Set Source Camera Attribution
SIBGRAPI '12 Proceedings of the 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Camera attribution approaches in digital image forensics have most often been evaluated in a closed set context, whereby all devices are known during training and testing time. However, in a real investigation, we must assume that innocuous images from unknown devices will be recovered, which we would like to remove from the pool of evidence. In pattern recognition, this corresponds to what is known as the open set recognition problem. This article introduces new algorithms for open set modes of image source attribution (identifying whether or not an image was captured by a specific digital camera) and device linking (identifying whether or not a pair of images was acquired from the same digital camera without the need for physical access to the device). Both algorithms rely on a new multi-region feature generation strategy, which serves as a projection space for the class of interest and emphasizes its properties, and on decision boundary carving, a novel method that models the decision space of a trained SVM classifier by taking advantage of a few known cameras to adjust the decision boundaries to decrease false matches from unknown classes. Experiments including thousands of unconstrained images collected from the web show a significant advantage for our approaches over the most competitive prior work.