ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results

• URL: http://arxiv.org/abs/2112.08325v1
• Date: Wed, 15 Dec 2021 18:17:55 GMT
• Title: ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results
• Authors: Yinan He, Lu Sheng, Jing Shao, Ziwei Liu, Zhaofan Zou, Zhizhi Guo, Shan Jiang, Curitis Sun, Guosheng Zhang, Keyao Wang, Haixiao Yue, Zhibin Hong, Wanguo Wang, Zhenyu Li, Qi Wang, Zhenli Wang, Ronghao Xu, Mingwen Zhang, Zhiheng Wang, Zhenhang Huang, Tianming Zhang, Ningning Zhao
• Abstract summary: This paper reports methods and results in the ForgeryNet - Face Forgery Analysis Challenge 2021, which employs the ForgeryNet benchmark. A total of 186 participants registered for the competition, and 11 teams made valid submissions.
• Score: 34.73701165667725
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The rapid progress of photorealistic synthesis techniques has reached a critical point where the boundary between real and manipulated images starts to blur. Recently, a mega-scale deep face forgery dataset, ForgeryNet which comprised of 2.9 million images and 221,247 videos has been released. It is by far the largest publicly available in terms of data-scale, manipulations (7 image-level approaches, 8 video-level approaches), perturbations (36 independent and more mixed perturbations), and annotations (6.3 million classification labels, 2.9 million manipulated area annotations, and 221,247 temporal forgery segment labels). This paper reports methods and results in the ForgeryNet - Face Forgery Analysis Challenge 2021, which employs the ForgeryNet benchmark. The model evaluation is conducted offline on the private test set. A total of 186 participants registered for the competition, and 11 teams made valid submissions. We will analyze the top-ranked solutions and present some discussion on future work directions.

Related papers

• AIM 2022 Challenge on Instagram Filter Removal: Methods and Results [66.98814754338841]
  This paper introduces the methods and the results of AIM 2022 challenge on Instagram Filter Removal. The main goal of this challenge is to produce realistic and visually plausible images where the impact of the filters applied is mitigated while preserving the content. There are two prior studies on this task as the baseline, and a total of 9 teams have competed in the final phase of the challenge.
  arXiv  Detail & Related papers  (2022-10-17T12:21:59Z)
• Results and findings of the 2021 Image Similarity Challenge [43.79331237080075]
  The 2021 Image Similarity Challenge introduced a dataset to serve as a new benchmark to evaluate recent image copy detection methods. This paper presents a quantitative and qualitative analysis of the top submissions.
  arXiv  Detail & Related papers  (2022-02-08T17:23:32Z)
• Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets [90.61266099147053]
  We investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. We propose modifications and best practices aimed at minimizing human labeling effort. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average.
  arXiv  Detail & Related papers  (2021-04-26T16:29:32Z)
• ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis [36.49022293347066]
  ForgeryNet is an extremely large face forgery dataset with unified annotations in image- and video-level data. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale.
  arXiv  Detail & Related papers  (2021-03-09T18:58:32Z)
• Contemplating real-world object classification [53.10151901863263]
  We reanalyze the ObjectNet dataset recently proposed by Barbu et al. containing objects in daily life situations. We find that applying deep models to the isolated objects, rather than the entire scene as is done in the original paper, results in around 20-30% performance improvement.
  arXiv  Detail & Related papers  (2021-03-08T23:29:59Z)
• DeeperForensics Challenge 2020 on Real-World Face Forgery Detection: Methods and Results [144.5252578415748]
  This paper reports methods and results in the DeeperForensics Challenge 2020 on real-world face forgery detection. The challenge employs the DeeperForensics-1.0 dataset, with 60,000 videos constituted by a total of 17.6 million frames. A total of 115 participants registered for the competition, and 25 teams made valid submissions.
  arXiv  Detail & Related papers  (2021-02-18T16:48:57Z)
• Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels [128.77822070156057]
  In this work, we quantitatively analyze label problems that objects may explicitly or implicitly have multiple labels. We propose a soft-sampling methods with hybrid training scheduler to deal with the label imbalance. Our method yields a dramatic improvement of 3.34 points, leading to the best single model with 60.90 mAP on the public object detection test set of Open Images.
  arXiv  Detail & Related papers  (2020-05-18T04:36:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.