Learnable Information-Preserving Image Resizer for Face Forgery Detection

Resizing input face images of arbitrary sizes to a uniform size is an essential preprocessing to satisfy the architectural requirements of face forgery detectors. In this letter, we reveal an important observation that traditional resizing methods degrade the performance of face forgery detectors due to the loss of high-frequency information. To address this issue, we propose a simple yet effective learnable information-preserving resizer to replace its lossy traditional counterparts. Specifically, we use Haar transform to separate low- and high-frequency components, and then perform learnable resizing on the high-frequency sub-bands. We conduct experiments to compare our learnable resizer with other methods and evaluate three existing detectors with and without incorporating our resizer. Experimental results show that our resizer outperforms other resizers and consistently enhances the detection performance of tested detectors, confirming the effectiveness of our proposed resizer.