Results 21 to 30 of about 4,729,114 (317)
Exploring Temporal Coherence for More General Video Face Forgery Detection [PDF]
IEEE International Conference on Computer Vision, 2021 Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos.
Yinglin Zheng +4 more
semanticscholar +1 more source
TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization [PDF]
Computer Vision and Pattern Recognition, 2022 In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning.
Fabrizio Guillaro +4 more
semanticscholar +1 more source
Blind inpainting forgery detection [PDF]
2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2014 The increasingly use of digital images in our daily life and the availability of powerful software for processing and editing images, open new challenges regarding illegal or unauthorized image manipulation. Thus, it becomes essential to authenticate digital copies, validate their content, and detect possible forgeries.
Dang Thanh Trung +2 more
openaire +1 more source
Face X-Ray for More General Face Forgery Detection [PDF]
Computer Vision and Pattern Recognition, 2019 In this paper we propose a novel image representation called face X-ray for detecting forgery in face images. The face X-ray of an input face image is a greyscale image that reveals whether the input image can be decomposed into the blending of two ...
Lingzhi Li +6 more
semanticscholar +1 more source
MesoNet: a Compact Facial Video Forgery Detection Network [PDF]
International Workshop on Information Forensics and Security, 2018 This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face.
Darius Afchar +3 more
semanticscholar +1 more source
Image Forgery Detection with Interpretability
CoRR, 2022 In this work, we present a learning based method focusing on the convolutional neural network (CNN) architecture to detect these forgeries. We consider the detection of both copy-move forgeries and inpainting based forgeries. For these, we synthesize our own large dataset.
Ankit Katiyar, Arnav Bhavsar
openaire +2 more sources
Local Relation Learning for Face Forgery Detection [PDF]
AAAI Conference on Artificial Intelligence, 2021 With the rapid development of facial manipulation techniques, face forgery has received considerable attention in digital media forensics due to security concerns.
Shen Chen +5 more
semanticscholar +1 more source
CoRR, 2020 Rapid progress in deep learning is continuously making it easier and cheaper to generate video forgeries. Hence, it becomes very important to have a reliable way of detecting these forgeries. This paper describes such an approach for various tampering scenarios. The problem is modelled as a per-frame binary classification task.
Nika Dogonadze +2 more
openaire +2 more sources
Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
Applied Sciences, 2023 Image copy-move forgery is a common simple tampering technique. To address issues such as high time complexity in most copy-move forgery detection algorithms and difficulty detecting forgeries in smooth regions, this paper proposes an image copy-move ...
Guiwei Fu, Yujin Zhang, Yongqi Wang
doaj +1 more source
Copy-move forgery detection using the segment gradient orientation histogram [PDF]
, 2017 The ready availability of image-editing software makes ensuring the authenticity of images an important issue. The most common type of image tampering is cloning, or Copy-Move Forgery (CMF), in which part(s) of the image are copied and pasted back into ...
Khayeat, Ali +2 more
core +2 more sources