| Title |
Super-resolution Detection based on Deepfake Image Manipulation Techniques and Learnable Convolutional Kernels |
| Authors |
최기윤(Giyun Choi) ; 윤성빈(Sungbin Youn) ; 최종원(Jongwon Choi) |
| DOI |
https://doi.org/10.5573/ieie.2025.62.10.42 |
| Keywords |
Super-resolution detection; Deepfake detection; Deepfake manipulation; Learnable convolutional kernel; Digital forensic |
| Abstract |
In this paper, we propose a novel detection model to distinguish between original images and super-resolution (SR) images generated by both GAN-based algorithms (e.g., SRGAN, EDSR, RCAN) and a commercial tool (TOPAZ Labs). Previous deepfake detection studies have attempted to improve performance by leveraging frequency-domain characteristics or pre-trained backbone networks; however, they often suffered from limited adaptability, excelling only on specific algorithms or datasets. To address this, we introduce a framework that extracts fine-grained, local features of super-resolution images via learnable convolution kernels (LCKs) and leverages a ResNet-50 model pre-trained to robustly handle various image manipulations. Our approach demonstrates exceptional detection accuracy across diverse SR datasets, achieving a 100% success rate on all test sets. Furthermore, gradient map visualizations reveal that the proposed model effectively focuses on critical image details, such as edges and textures, to discriminate super-resolution images from their originals. |