Image authentication based on image hashing has gained large attention in recent years. However, limited work has been done in color image hashing. Also, most of the existing methods are unable to detect tampering, if the composite rotation–scaling–translation (RST) distortion and tampering in a color image occur simultaneously. In this paper, an image hashing technique has been proposed based on convolutional stacked denoising auto-encoders (CSDAEs). In addition, a blind geometric correction approach is used to correct the composite RST distortion in the image. An input image is hierarchically mapped to a lower-dimensional hash code via CSDAEs, which have been trained for content-preserving operations (CPOs). An image map is generated from the hash via the decoder. The tampered area has been localized, by comparing the image map of hash codes from the reference image and the received image. The experimental results show that the proposed method is robust against most of the CPOs, especially to composite RST, a better trade-off between robustness and discrimination, and can localize the tampered regions. The receiver operating characteristics show that the proposed model is better than some of the state-of-the-art methods.

近年来，基于图像哈希的图像认证受到了广泛的关注。但是，在彩色图像哈希处理中所做的工作有限。此外，如果彩色图像中的旋转，缩放，平移（RST）复合失真和篡改同时发生，则大多数现有方法都无法检测到篡改。本文提出了一种基于卷积堆叠去噪自动编码器（CSDAE）的图像哈希技术。另外，使用盲几何校正方法来校正图像中的合成RST失真。输入图像通过CSDAE（已针对内容保存操作（CPO）进行了训练）被层次结构地映射到低维哈希码。经由解码器从散列生成图像映射。篡改区域已本地化，通过比较来自参考图像和接收图像的哈希码的图像图。实验结果表明，该方法对大多数CPO都具有鲁棒性，尤其是对于复合RST，在鲁棒性和辨别力之间有更好的折衷，并且可以定位被篡改的区域。接收机的工作特性表明，所提出的模型优于某些最新方法。