散列函数
卷积神经网络
计算机科学
特征哈希
稳健性(进化)
人工智能
动态完美哈希
联营
模式识别(心理学)
认证(法律)
约束(计算机辅助设计)
深度学习
哈希表
数学
双重哈希
基因
生物化学
计算机安全
化学
几何学
作者
Chuan Qin,Enli Liu,Guorui Feng,Xinpeng Zhang
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2021-11-01
卷期号:31 (11): 4523-4537
被引量:20
标识
DOI:10.1109/tcsvt.2020.3047142
摘要
In this paper, a novel perceptual image hashing scheme based on convolutional neural network (CNN) with multiple constraints is proposed, in which our deep hashing network learns the process of features extraction automatically according to the training target and then generates the final hash sequence. The combination of convolutional and pooling layers is to reduce the size of input image while deepening the channels. Then, we construct two pairs of constraints and integrate them into an overall constraint function through a strategy of weight allocation. In order to guarantee the robustness and discrimination of deep hashing network simultaneously, a new training method is developed to adjust the training set structure dynamically according to the changes of constraint values. Experimental results show that the proposed deep hashing network can achieve a satisfactory balance between perceptual robustnzess and discrimination while maintaining security. Based on the large-scale test set, receiver operating characteristic (ROC) curves, ${F}_{1}$ scores and equal error rate (EER) demonstrate the superiority of our scheme in terms of content authentication compared with some state-of-the-art schemes.
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