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The detection of AI-generated faces is commonly approached as a binary classification task. Nevertheless, the resulting detectors frequently struggle to adapt to novel AI face generators, which evolve rapidly. In this paper, we describe an…

Computer Vision and Pattern Recognition · Computer Science 2025-01-07 Mian Zou , Baosheng Yu , Yibing Zhan , Kede Ma

Face-swap DeepFake is an emerging AI-based face forgery technique that can replace the original face in a video with a generated face of the target identity while retaining consistent facial attributes such as expression and orientation.…

Computer Vision and Pattern Recognition · Computer Science 2024-04-23 Pu Sun , Honggang Qi , Yuezun Li , Siwei Lyu

Deep vision models are now mature enough to be integrated in industrial and possibly critical applications such as autonomous navigation. Yet, data collection and labeling to train such models requires too much efforts and costs for a…

Machine Learning · Computer Science 2025-10-24 Estelle Chigot , Dennis G. Wilson , Meriem Ghrib , Fabrice Jimenez , Thomas Oberlin

Synthetic images, audio, and video can now be generated and edited by Artificial Intelligence (AI). In particular, the malicious use of synthetic data has raised concerns about potential harms to cybersecurity, personal privacy, and public…

Human-Computer Interaction · Computer Science 2025-08-05 Yingfan Zhou , Ester Chen , Manasa Pisipati , Aiping Xiong , Sarah Rajtmajer

Deeplearning has been used to solve complex problems in various domains. As it advances, it also creates applications which become a major threat to our privacy, security and even to our Democracy. Such an application which is being…

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

Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can…

Computer Vision and Pattern Recognition · Computer Science 2022-08-12 Thanh Thi Nguyen , Quoc Viet Hung Nguyen , Dung Tien Nguyen , Duc Thanh Nguyen , Thien Huynh-The , Saeid Nahavandi , Thanh Tam Nguyen , Quoc-Viet Pham , Cuong M. Nguyen

Recent advances in generative modeling can create remarkably realistic synthetic videos, making it increasingly difficult for humans to distinguish them from real ones and necessitating reliable detection methods. However, two key…

Computer Vision and Pattern Recognition · Computer Science 2026-01-19 Long Ma , Zihao Xue , Yan Wang , Zhiyuan Yan , Jin Xu , Xiaorui Jiang , Haiyang Yu , Yong Liao , Zhen Bi

Recent advances in AI technology have made the forgery of digital images and videos easier, and it has become significantly more difficult to identify such forgeries. These forgeries, if disseminated with malicious intent, can negatively…

Computer Vision and Pattern Recognition · Computer Science 2019-12-30 Chia-Mu Yu , Ching-Tang Chang , Yen-Wu Ti

Successful forensic detectors can produce excellent results in supervised learning benchmarks but struggle to transfer to real-world applications. We believe this limitation is largely due to inadequate training data quality. While most…

Computer Vision and Pattern Recognition · Computer Science 2025-04-04 Fabrizio Guillaro , Giada Zingarini , Ben Usman , Avneesh Sud , Davide Cozzolino , Luisa Verdoliva

With growing abilities of generative models, artificial content detection becomes an increasingly important and difficult task. However, all popular approaches to this problem suffer from poor generalization across domains and generative…

Computer Vision and Pattern Recognition · Computer Science 2024-06-24 Tatiana Gaintseva , Laida Kushnareva , German Magai , Irina Piontkovskaya , Sergey Nikolenko , Martin Benning , Serguei Barannikov , Gregory Slabaugh

Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of…

Computer Vision and Pattern Recognition · Computer Science 2023-08-31 Anh-Khoa Nguyen Vu , Thanh-Toan Do , Vinh-Tiep Nguyen , Tam Le , Minh-Triet Tran , Tam V. Nguyen

Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce…

Computer Vision and Pattern Recognition · Computer Science 2020-09-14 Luca Guarnera , Oliver Giudice , Sebastiano Battiato

In recent years, deep learning methods have become increasingly capable of generating near photorealistic pictures and humanlike text up to the point that humans can no longer recognize what is real and what is AI-generated. Concerningly,…

Social and Information Networks · Computer Science 2022-09-16 Sippo Rossi , Youngjin Kwon , Odd Harald Auglend , Raghava Rao Mukkamala , Matti Rossi , Jason Thatcher

For successful scene text recognition (STR) models, synthetic text image generators have alleviated the lack of annotated text images from the real world. Specifically, they generate multiple text images with diverse backgrounds, font…

Computer Vision and Pattern Recognition · Computer Science 2021-07-21 Moonbin Yim , Yoonsik Kim , Han-Cheol Cho , Sungrae Park

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

The ability of image and video generation models to create photorealistic images has reached unprecedented heights, making it difficult to distinguish between real and fake images in many cases. However, despite this progress, a gap remains…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Ali Borji

Recent generative models produce images with a level of authenticity that makes them nearly indistinguishable from real photos and artwork. Potential harmful use cases of these models, necessitate the creation of robust synthetic image…

Computer Vision and Pattern Recognition · Computer Science 2025-01-15 Delyan Boychev , Radostin Cholakov

Recent advancements in video diffusion models enable the generation of photorealistic videos with impressive 3D consistency and temporal coherence. However, the extent to which these AI-generated videos simulate the 3D visual world remains…

Computer Vision and Pattern Recognition · Computer Science 2025-10-07 Chirui Chang , Jiahui Liu , Zhengzhe Liu , Xiaoyang Lyu , Yi-Hua Huang , Xin Tao , Pengfei Wan , Di Zhang , Xiaojuan Qi

Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photography devices or created by human artists (i.e., real…

Computer Vision and Pattern Recognition · Computer Science 2024-04-24 Yuying Li , Zeyan Liu , Junyi Zhao , Liangqin Ren , Fengjun Li , Jiebo Luo , Bo Luo

The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report delves into the potential and limitations of generative deep…

Computers and Society · Computer Science 2024-03-20 Melanie Mathys , Marco Willi , Michael Graber , Raphael Meier
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