WAN: Watermarking Attack Network

08/14/2020
by   Seung-Hun Nam, et al.
0

Multi-bit watermarking (MW) has been developed to improve robustness against signal processing operations and geometric distortions. To this end, benchmark tools that test robustness by applying simulated attacks on watermarked images are available. However, limitations in these general attacks exist since they cannot exploit specific characteristics of the targeted MW. In addition, these attacks are usually devised without consideration of visual quality, which rarely occurs in the real world. To address these limitations, we propose a watermarking attack network (WAN), a fully trainable watermarking benchmark tool that utilizes the weak points of the target MW and induces an inversion of the watermark bit, thereby considerably reducing the watermark extractability. To hinder the extraction of hidden information while ensuring high visual quality, we utilize a residual dense blocks-based architecture specialized in local and global feature learning. A novel watermarking attack loss is introduced to break the MW systems. We empirically demonstrate that the WAN can successfully fool various block-based MW systems.

READ FULL TEXT

page 1

page 2

page 3

page 6

research
05/16/2018

Robust curvelet domain watermarking technique that preserves cleanness of high quality images

Watermarking inserts invisible data into content to protect copyright. T...
research
08/18/2021

MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression

Based on the powerful feature extraction ability of deep learning archit...
research
06/29/2023

Group-based Robustness: A General Framework for Customized Robustness in the Real World

Machine-learning models are known to be vulnerable to evasion attacks th...
research
10/12/2021

Hiding Images into Images with Real-world Robustness

The existing image embedding networks are basically vulnerable to malici...
research
04/11/2017

A Robust Blind Watermarking Using Convolutional Neural Network

This paper introduces a blind watermarking based on a convolutional neur...
research
12/21/2020

On Success and Simplicity: A Second Look at Transferable Targeted Attacks

There is broad consensus among researchers studying adversarial examples...
research
03/13/2022

Label-only Model Inversion Attack: The Attack that Requires the Least Information

In a model inversion attack, an adversary attempts to reconstruct the da...

Please sign up or login with your details

Forgot password? Click here to reset