Two-branch Multi-scale Deep Neural Network for Generalized Document Recapture Attack Detection

11/30/2022
by   Jiaxing Li, et al.
0

The image recapture attack is an effective image manipulation method to erase certain forensic traces, and when targeting on personal document images, it poses a great threat to the security of e-commerce and other web applications. Considering the current learning-based methods suffer from serious overfitting problem, in this paper, we propose a novel two-branch deep neural network by mining better generalized recapture artifacts with a designed frequency filter bank and multi-scale cross-attention fusion module. In the extensive experiment, we show that our method can achieve better generalization capability compared with state-of-the-art techniques on different scenarios.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/25/2022

Unsupervised Image Fusion Method based on Feature Mutual Mapping

Deep learning-based image fusion approaches have obtained wide attention...
research
04/20/2021

M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

The widespread dissemination of forged images generated by Deepfake tech...
research
07/02/2022

Noise and Edge Based Dual Branch Image Manipulation Detection

Unlike ordinary computer vision tasks that focus more on the semantic co...
research
04/04/2019

Deep Multi-scale Discriminative Networks for Double JPEG Compression Forensics

As JPEG is the most widely used image format, the importance of tamperin...
research
07/28/2023

DocDeshadower: Frequency-aware Transformer for Document Shadow Removal

The presence of shadows significantly impacts the visual quality of scan...
research
03/27/2022

Adaptive Frequency Learning in Two-branch Face Forgery Detection

Face forgery has attracted increasing attention in recent applications o...
research
04/05/2020

Deep Homography Estimation for Dynamic Scenes

Homography estimation is an important step in many computer vision probl...

Please sign up or login with your details

Forgot password? Click here to reset