Improving the Security of Image Manipulation Detection through One-and-a-half-class Multiple Classification

by

journal Paper1
Authors: Mauro Barni, Ehsan Nowroozi, Benedetta Tondi
Year: 1019
Abstract: Protecting image manipulation detectors against perfect knowledge attacks requires the adoption of detector architectures which are intrinsically difficult to attack. In this paper, we do so, by exploiting a recently proposed multiple-classifier architecture combining the improved security of 1-Class (1C) classification and the good performance ensured by conventional 2-Class (2C) classification in the absence of attacks. The architecture, also known as 1.5-Class (1.5C) classifier, consists of one 2C classifier and two 1C classifiers run in parallel followed by a final 1C classifier…..

VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

by

journal Paper1
Authors: Anselmo Ferreira, Ehsan Nowroozi, Mauro Barni
Year: 2021
Abstract: The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned…..

Higher-Order, Adversary-Aware, Double JPEG-Detection via Selected Training on Attacked Samples

by

Conference Paper1
Authors: Mauro Barni, Ehsan Nowroozi, Benedetta Tondi
Year: 2017
Abstract: In this paper we present an adversary-aware double JPEG detector which is capable of detecting the presence of two JPEG compression steps even in the presence of heterogeneous processing and counter-forensic (C-F) attacks. The detector is based on an SVM classifier fed with a large number of features and trained to recognise the traces left by double JPEG detection in the presence of attacks. Since it is not possible to train the SVM on all possible kinds of processing and C-F attacks, a selected set of images, manipulated with a limited number of attacks is added to the training set……

Detection of Adaptive Histogram Equalization Robust Against JPEG Compression

by

Conference Paper1
Authors: Mauro Barni, Ehsan Nowroozi, Benedetta Tondi
Year: 2018
Abstract: Contrast Enhancement (CE) detection in the presence of laundering attacks, i.e. common processing operators applied with the goal to erase the traces the CE detector looks for, is a challenging task. JPEG compression is one of the most harmful laundering attacks, which has been proven to deceive most CE detectors proposed so far. In this paper, we present a system that is able to detect contrast enhancement by means of adaptive histogram equalization in the presence of JPEG compression, by training a JPEG-aware SVM detector based on color SPAM features, i.e., an SVM detector trained on contrast-enhanced-then-JPEG-compressed images……