Search Results for author:
Found 7 papers, 4 papers with code
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources.
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources.
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
In this paper, we study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods, e. g., GANs and diffusion models.
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse.
CLE Diffusion: Controllable Light Enhancement Diffusion Model
Low light enhancement has gained increasing importance with the rapid development of visual creation and editing.
Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection
The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models.
LID 2020: The Learning from Imperfect Data Challenge Results
The purpose of the Learning from Imperfect Data (LID) workshop is to inspire and facilitate the research in developing novel approaches that would harness the imperfect data and improve the data-efficiency during training.
