TLDR: We propose HiDA-Net, a detector for high-resolution AI-generated images that leverages all input pixels by integrating global context with local tile features, achieving 13% gain on challenging Chameleon benchmark.
The rapid growth of high-resolution, meticulously crafted AI-generated images poses a significant challenge to existing detection methods, which are often trained and evaluated on low-resolution, automatically generated datasets that do not align with the complexities of high-resolution scenarios. A common practice is to resize or center-crop high-resolution images to fit standard network inputs. However, without full coverage of all pixels, such strategies risk either obscuring subtle, high-frequency artifacts or discarding information from uncovered regions, leading to input information loss.
In this paper, we introduce the High-Resolution Detail-Aggregation Network (HiDA-Net), a novel framework that ensures no pixel is left behind. We use the Feature Aggregation Module (FAM) module, which fuses features from multiple full-resolution local tiles with a down-sampled global view of the image. These local features are aggregated and fused with global representations for final prediction, ensuring that native-resolution details are preserved and utilized for detection. To enhance robustness against challenges such as localized AI manipulations and compression, we introduce Token-wise Forgery Localization (TFL) module for fine-grained spatial sensitivity and JPEG Quality Factor Estimation (QFE) module to disentangle generative artifacts from compression noise explicitly. Furthermore, to facilitate future research, we introduce HiRes-50K, a new challenging benchmark consisting of 50,568 images with up to 64 megapixels. Extensive experiments show that HiDA-Net achieves state-of-the-art, increasing accuracy by over 13% on the challenging Chameleon dataset and 10% on our HiRes-50K.
Create a virtual environment and install requirements.
pip install -r requirements.txt
pip install -e .FBCNN model is available from this link. Please change yaml files in .config folder and .utils/dataset_source.py to configure the settings.
# For training
torchrun --standalone --nproc-per-node=1 train_HiDA.py --config configs/train.yaml
# For evaluation
torchrun --standalone --nproc-per-node=1 evaluate.py --config configs/test.yaml --weights /path/to/pthYou can get this dataset from this link.