Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
The Video and Image Processing (VIP) Cup is a student competition that takes place each year at the IEEE International Conference on Image Processing. The 2022 IEEE VIP Cup asked undergraduate students to develop a system capable of distinguishing pristine images from generated ones. The interest in this topic stems from the incredible advances in the AI-based generation of visual data, with tools that allows the synthesis of highly realistic images and videos. While this opens up a large number of new opportunities, it also undermines the trustworthiness of media content and fosters the spread of disinformation on the internet. Recently there was strong concern about the generation of extremely realistic images by means of editing software that includes the recent technology on diffusion models. In this context, there is a need to develop robust and automatic tools for synthetic image detection.
Code (0)
등록된 구현이 없습니다.
Tasks
Synthetic Image DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]
Robust and reliable traffic sign detection is necessary to bring autonomous vehicles onto our roads. State-of-the-art algorithms successfully perform traffic sign detection over existing databases that mostly lack severe…
Autonomous VehiclesTraffic Sign DetectionOphthalmic Biomarker Detection: Highlights from the IEEE Video and Image Processing Cup 2023 Student Competition
The VIP Cup offers a unique experience to undergraduates, allowing students to work together to solve challenging, real-world problems with video and image processing techniques. In this iteration of the VIP Cup, we chal…
Diabetic Retinopathy Detection Based on Convolutional Neural Networks with SMOTE and CLAHE Techniques Applied to Fundus Images
Diabetic retinopathy (DR) is one of the major complications in diabetic patients' eyes, potentially leading to permanent blindness if not detected timely. This study aims to evaluate the accuracy of artificial intelligen…
Binary ClassificationDiabetic Retinopathy DetectionA 2-Stage Model for Vehicle Class and Orientation Detection with Photo-Realistic Image Generation
We aim to detect the class and orientation of a vehicle by training a model with synthetic data. However, the distribution of the classes in the training data is imbalanced, and the model trained on the synthetic image i…
Image GenerationVideoHallu: Evaluating and Mitigating Multi-modal Hallucinations on Synthetic Video Understanding
Synthetic video generation has gained significant attention for its realism and broad applications, but remains prone to violations of common sense and physical laws. This highlights the need for reliable abnormality det…
Anomaly DetectionCommon Sense ReasoningHallucinationMVBench+2