摘要： 随着多媒体技术的发展，未经授权伪造和传播虚假信息的难度大大降低，可能引发一系列负面后果，亟需有效的内容认证方法以确保图像内容的真实性和安全性。近年来，感知图像哈希在图像认证领域展现出了非常优越的性能，然而，现有算法在处理文字占比较大的图像时效果并不理想，也无法有效应对划线等新型内容保留操作。因此，提出了一种基于感知哈希的图文图像内容认证算法。所提算法采用了环分割的图像划分方法，统计了每个环内尺度不变特征变换(SIFT)关键点的频率特征和分布特征，这些特征具有旋转不变性，可以有效提升所提算法的抗冲突性。通过获取关键点信息，所提算法对包括不规则划线在内的内容保留操作都具有良好的鲁棒性。构建了一个图文图像(TPMI)数据集对所提算法进行实验，与一些有代表性的算法相比，该算法在感知鲁棒性、抗冲突性和安全性方面都具有更好的性能。对于图像进行部分的篡改，能够很好的将每个篡改图像判断为与原始图像相似。此外，还针对现实中常见的划线攻击进行了实验，结果表明能够有效识别这类攻击图像。

Abstract: With the development of multimedia technology, the difficulty of unauthorized forgery and dissemination of false information has greatly decreased. This may lead to a series of negative consequences. Effective content authentication algorithms are urgently needed to ensure the authenticity and security of image content. In recent years, perceptual image hashing has shown excellent performance in the field of image authentication. However, existing algorithms are not ideal for processing images with a large proportion of text, and they can not effectively cope with new content-preservation manipulations such as scribble. Therefore, a text-picture mixed image content authentication algorithm based on perceptual hashing is proposed. The proposed algorithm adopts the image segmentation algorithm of ring partition, and it calculates the frequency and distribution characteristics of SIFT key points within each ring. These features have rotation invariance and can effectively improve the anti-collision performance of the proposed algorithm. By obtaining key point information, the proposed algorithm performs good robustness performance against content-preservation manipulations, including irregular scribble. A Text-Picture Mixed Image (TPMI) dataset is constructed to validate the performance of the proposed algorithm. Compared with some representative algorithms, this algorithm has better performance in perceptual robustness, anti-collision, and security. Partial tampering with images can effectively identify each tampered image as similar to the original image. In addition, experiments on scribble attacks are constructed in reality, and the results show that it can effectively identify such attack images.