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    基于窗口自注意力特征融合的感知图像认证哈希

    Perceptual Authentication Hashing with Image Feature Fusion Based on Window Self-Attention

    • 摘要: 随着多媒体和互联网技术的快速发展,数字图像内容的安全性问题日益突出. 为此,提出了一种基于窗口自注意力特征融合的深度感知图像认证哈希方案,该方案能有效检测原始图像的感知内容是否发生变化,并可应用于内容认证、复制检测、篡改识别等场合. 该方案以卷积神经网络为基础,利用窗口自注意力构建了一个融合图像全局和局部特征的哈希模型. 模型首先对主干网络获得的浅层特征进行分块并提取相应的窗口特征,然后计算每个局部特征与全局特征之间的相关性来筛选出最终的局部特征,再将这部分特征和全局特征输入到哈希生成模块中进行融合与压缩,得到最终的图像哈希码. 在训练过程中,利用哈希损失和分类损失构造的联合损失函数对模型进行约束,提高感知认证哈希方案的鲁棒性和唯一性. 实验结果表明,与现有典型的感知认证哈希方案相比,该方案可获得更优的图像内容认证性能.

       

      Abstract: With the rapid development of multimedia and network technology, the security of digital image content is becoming more and more prominent. In this paper, we propose a deep perceptual image authentication hashing schema based on window self-attention feature fusion, that can effectively detect whether the perceptual content of the original image has changed. It can be applied to content authentication, tampering recognition, copy detection, and other similar scenarios. This model uses a convolutional neural network architecture that integrates a window self-attention mechanism to build a hashing model that encompasses global and local image features. The model chunks the shallow features obtained from the backbone network and extracts the corresponding window features, then calculates the correlation between each intermediate local feature and the global feature to filter out the final local features, and finally inputs the local features and global features into the hash generation module for fusion and compression to obtain the final image hash code. In the training process, an integrated loss function based on hash loss and classification loss is used to constrain the model to improve the robustness and discrimination. The experimental results show that this scheme can achieve superior image content authentication performance compared with existing typical perceptual authentication hashing schemes.

       

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