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Related papers: Exposing DeepFakes via Hyperspectral Domain Mappin…

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Generating synthetic fake faces, known as pseudo-fake faces, is an effective way to improve the generalization of DeepFake detection. Existing methods typically generate these faces by blending real or fake faces in spatial domain. While…

Computer Vision and Pattern Recognition · Computer Science 2024-11-26 Hanzhe Li , Jiaran Zhou , Yuezun Li , Baoyuan Wu , Bin Li , Junyu Dong

In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from single pristine images, reproducing common forgery artifacts…

Computer Vision and Pattern Recognition · Computer Science 2022-04-19 Kaede Shiohara , Toshihiko Yamasaki

Facial manipulation by deep fake has caused major security risks and raised severe societal concerns. As a countermeasure, a number of deep fake detection methods have been proposed recently. Most of them model deep fake detection as a…

Computer Vision and Pattern Recognition · Computer Science 2022-04-12 Aakash Varma Nadimpalli , Ajita Rattani

Recent progress in generative AI, primarily through diffusion models, presents significant challenges for real-world deepfake detection. The increased realism in image details, diverse content, and widespread accessibility to the general…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Chaitali Bhattacharyya , Hanxiao Wang , Feng Zhang , Sungho Kim , Xiatian Zhu

We propose an end-to-end pipeline for both building and tracking 3D facial models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.) video data. First, we present a method for automatic data curation and retrieval based…

Computer Vision and Pattern Recognition · Computer Science 2022-04-28 Winnie Lin , Yilin Zhu , Demi Guo , Ron Fedkiw

The widespread dissemination of Deepfakes demands effective approaches that can detect perceptually convincing forged images. In this paper, we aim to capture the subtle manipulation artifacts at different scales using transformer models.…

Computer Vision and Pattern Recognition · Computer Science 2022-04-20 Junke Wang , Zuxuan Wu , Wenhao Ouyang , Xintong Han , Jingjing Chen , Ser-Nam Lim , Yu-Gang Jiang

Hyperspectral Imagery (HSI) has been used in many applications to non-destructively determine the material and/or chemical compositions of samples. There is growing interest in creating 3D hyperspectral reconstructions, which could provide…

Computer Vision and Pattern Recognition · Computer Science 2024-03-25 Gerry Chen , Sunil Kumar Narayanan , Thomas Gautier Ottou , Benjamin Missaoui , Harsh Muriki , Cédric Pradalier , Yongsheng Chen

The current high-fidelity generation and high-precision detection of DeepFake images are at an arms race. We believe that producing DeepFakes that are highly realistic and 'detection evasive' can serve the ultimate goal of improving future…

Computer Vision and Pattern Recognition · Computer Science 2024-03-22 Yihao Huang , Felix Juefei-Xu , Qing Guo , Yang Liu , Geguang Pu

Deep learning has enabled realistic face manipulation (i.e., deepfake), which poses significant concerns over the integrity of the media in circulation. Most existing deep learning techniques for deepfake detection can achieve promising…

Computer Vision and Pattern Recognition · Computer Science 2022-12-26 Bosheng Yan , Chang-Tsun Li , Xuequan Lu

The rapid progress of generative adversarial networks (GANs) and diffusion models has enabled the creation of synthetic faces that are increasingly difficult to distinguish from real images. This progress, however, has also amplified the…

Computer Vision and Pattern Recognition · Computer Science 2026-04-01 Kyeonghun Kim , Youngung Han , Seoyoung Ju , Yeonju Jean , YooHyun Kim , Minseo Choi , SuYeon Lim , Kyungtae Park , Seungwoo Baek , Sieun Hyeon , Nam-Joon Kim , Hyuk-Jae Lee

Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically sensitive contexts. This challenge is amplified by the increasing realism of modern generative…

Computer Vision and Pattern Recognition · Computer Science 2025-10-08 Victor Livernoche , Akshatha Arodi , Andreea Musulan , Zachary Yang , Adam Salvail , Gaétan Marceau Caron , Jean-François Godbout , Reihaneh Rabbany

The rapid advancement of generative AI has enabled the creation of highly realistic forged facial images, posing significant threats to AI security, digital media integrity, and public trust. Face forgery techniques, ranging from face…

Computer Vision and Pattern Recognition · Computer Science 2025-10-29 Xin Zhang , Yuqi Song , Fei Zuo

Hyperspectral Imaging (HSI) captures rich spectral information across contiguous wavelength bands, supporting applications in precision agriculture, environmental monitoring, and autonomous driving. However, its high dimensionality poses…

Computer Vision and Pattern Recognition · Computer Science 2025-09-03 Yaniv Zimmer , Ofir Lindenbaum , Oren Glickman

Applications of deep learning to synthetic media generation allow the creation of convincing forgeries, called DeepFakes, with limited technical expertise. DeepFake detection is an increasingly active research area. In this paper, we…

Computer Vision and Pattern Recognition · Computer Science 2021-08-31 Kevin Lutz , Robert Bassett

Achieving accurate material segmentation for 3-channel RGB images is challenging due to the considerable variation in a material's appearance. Hyperspectral images, which are sets of spectral measurements sampled at multiple wavelengths,…

Computer Vision and Pattern Recognition · Computer Science 2023-08-21 Yuwen Heng , Yihong Wu , Jiawen Chen , Srinandan Dasmahapatra , Hansung Kim

Since photorealistic faces can be readily generated by facial manipulation technologies nowadays, potential malicious abuse of these technologies has drawn great concerns. Numerous deepfake detection methods are thus proposed. However,…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Rui Shao , Tianxing Wu , Ziwei Liu

Deep learning approaches have achieved highly accurate face recognition by training the models with very large face image datasets. Unlike the availability of large 2D face image datasets, there is a lack of large 3D face datasets available…

Computer Vision and Pattern Recognition · Computer Science 2021-12-23 Meng-Tzu Chiu , Hsun-Ying Cheng , Chien-Yi Wang , Shang-Hong Lai

Fusion-based hyperspectral image (HSI) super-resolution has become increasingly prevalent for its capability to integrate high-frequency spatial information from the paired high-resolution (HR) RGB reference image. However, most of the…

Computer Vision and Pattern Recognition · Computer Science 2023-02-14 Zeqiang Lai , Ying Fu , Jun Zhang

Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged face images. In response, various deepfake detection methods…

Computer Vision and Pattern Recognition · Computer Science 2023-07-07 Ruiyang Xia , Decheng Liu , Jie Li , Lin Yuan , Nannan Wang , Xinbo Gao

As of late an AI based free programming device has made it simple to make authentic face swaps in recordings that leaves barely any hints of control, in what are known as "deepfake" recordings. Situations where these genuine istic…

Computer Vision and Pattern Recognition · Computer Science 2020-09-17 Rahul U , Ragul M , Raja Vignesh K , Tejeswinee K