Results 261 to 270 of about 4,729,114 (317)

Where Deepfakes Gaze at? Spatial–Temporal Gaze Inconsistency Analysis for Video Face Forgery Detection

IEEE Transactions on Information Forensics and Security
With the continuous development of generative models on face generation, how to distinguish the real and fake face has become an important problem for security.
Chunlei Peng, Zimin Miao, Decheng Liu, Nannan Wang, Ruimin Hu, Xinbo Gao   +5 more
semanticscholar   +1 more source

Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection

International Conference on Learning Representations
The proliferation of face forgery techniques has raised significant concerns within society, thereby motivating the development of face forgery detection methods.
Jiawei Liang, Siyuan Liang, Aishan Liu, Xiaojun Jia, Junhao Kuang, Xiaochun Cao   +5 more
semanticscholar   +1 more source

DPMSN: A Dual-Pathway Multiscale Network for Image Forgery Detection

IEEE Transactions on Industrial Informatics
Multimedia images have become an important way for the sharing of digital information. However, advanced editing tools provide easy methods for malicious content modification, resulting in less viable information.
Nianyin Zeng, Peishu Wu, Yuqing Zhang, Han Li, Jingfeng Mao, Zidong Wang   +5 more
semanticscholar   +1 more source

MCS-GAN: A Different Understanding for Generalization of Deep Forgery Detection

IEEE transactions on multimedia
After several years of development, deep synthesis technology has made significant progress in image and video synthesis. Deep forgery represented by Deepfakes has become a research hotspot, which is used as a tool for disinformation attacks. The current
Shuai Xiao, Guipeng Lan, Jiachen Yang, Wen Lu, Qinggang Meng, Xinbo Gao   +5 more
semanticscholar   +1 more source

Language-Guided Hierarchical Fine-Grained Image Forgery Detection and Localization

International Journal of Computer Vision
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging.
Xiao Guo, Xiaohong Liu, I. Masi, Xiaoming Liu   +3 more
semanticscholar   +1 more source

Adaptive Texture and Spectrum Clue Mining for Generalizable Face Forgery Detection

IEEE Transactions on Information Forensics and Security
Although existing face forgery detection methods achieve satisfactory performance under closed within-dataset scenario where training and testing sets are created by the same manipulation technique, they are vulnerable to samples created by unseen ...
Jiawei Liu, Jingyi Xie, Yang Wang, Zheng-Jun Zha   +3 more
semanticscholar   +1 more source

Innovations in Image Forensics: Designing a Smart Learning Based Methodology for Forgery Detection over Digital Images

2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)
Detecting forgery in digital images is crucial for several reasons. Firstly, with the widespread use and accessibility of image editing tools, individuals can easily manipulate digital images, raising concerns about the authenticity and integrity of ...
G. Ramkumar, S.Rathika, K.Pithamber, Anni Princy, C.H.Srilakshmi, Vijay Anand Kandaswamy   +5 more
semanticscholar   +1 more source

Detecting art forgeries

Physics Today, 1980
Art forgery is a complex subject; it has as much to do with the personality of the faker or the salesman who markets the spurious work as it has to do with the recapturing of some past technique or mood. And it is a subject that is still highly active today, despite the unquestionably improved art historical scholarship of recent decades: the ...
openaire   +1 more source

Forgery Detection in 3D-Sensor Images

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018
The field of Image Forensic, and with it the notion of image forgery and its detection, is widely studied in 2D images and videos. Since 3D cameras (cameras with depth sensors) are becoming increasingly commonplace, it is of importance to introduce the notion of forgery detection in depth-images.
Noa Privman-Horesh, Azmi Haider, Hagit Hel-Or   +2 more
openaire   +1 more source

Audio-Visual Temporal Forgery Detection Using Embedding-Level Fusion and Multi-Dimensional Contrastive Loss

IEEE transactions on circuits and systems for video technology (Print)
Audio-visual deepfake detection is the process of identifying and detecting deepfakes that have been generated using both audio and visual content with AI algorithms.
Miao Liu, Jing Wang, Xinyuan Qian, Haizhou Li   +3 more
semanticscholar   +1 more source