A Novel Zero-Watermark Copyright Authentication Scheme Based on Lifting Wavelet and Harris Corner Detection

In this paper,a novel zero-watermark copyright authentication scheme based on Internet public certification system is proposed.This approach utilizes Haar integer wavelet transform based on a lifting scheme and adaptive Harris corner detection to extract image features,which will be used to produce a binary feature map,and the map is very crucial to the generation of watermark registered later.By properly choosing the parameters of aforementioned techniques such as the threshold T and the radius of local feature region R,the feature map is so much more stable and distinguishing that it can be used to construct robust watermark.Simulations demonstrate that the proposed scheme is resistant to many kinds of signal processing and malicious attacks such as Gaussian blurring,additive noising,JPEG lossy compression,cropping,scaling and slight rotation operation.Compared with a relative scheme such as that of Chang＇s,the scheme in this paper is more practicable and reliable and can be applied to the area of copyright protection.

Weakly Supervised Object Localization with Background Suppression Erasing for Art Authentication and Copyright Protection

The problem of art forgery and infringement is becoming increasingly prominent,since diverse self-media contents with all kinds of art pieces are released on the Internet every day.For art paintings,object detection and localization provide an efficient and ef-fective means of art authentication and copyright protection.However,the acquisition of a precise detector requires large amounts of ex-pensive pixel-level annotations.To alleviate this,we propose a novel weakly supervised object localization(WSOL)with background su-perposition erasing(BSE),which recognizes objects with inexpensive image-level labels.First,integrated adversarial erasing(IAE)for vanilla convolutional neural network(CNN)dropouts the most discriminative region by leveraging high-level semantic information.Second,a background suppression module(BSM)limits the activation area of the IAE to the object region through a self-guidance mechanism.Finally,in the inference phase,we utilize the refined importance map(RIM)of middle features to obtain class-agnostic loc-alization results.Extensive experiments are conducted on paintings,CUB-200-2011 and ILSVRC to validate the effectiveness of our BSE.