Abstract
In the last few decades, the discovery of various methods for generating secure image hash has become revolutionary in the field of image hashing. This paper presents an efficient approach to obtain image hash through DWT-SVD and a saliency detection technique using spectral residual model. The latest image hashing technique based on ring partition and invariant vector distance is rotation invariant for the large angle at the cost of being insensitive to corner forgery. But, due to the use of the central orientation information, the proposed system is rotation invariant for arbitrary angles along with sensitiveness to corner changes. In addition, we have used the HSV color space that gives desirable classification performance. It provides satisfactory results against large degree rotation, JPEG compression, brightness and contrast adjustment, watermarking, etc. This technique is also sensitive to malicious activities. Moreover, it locates the forged areas of a forged image. We have applied the proposed algorithm to a large collection of images from various databases. The receiver operating characteristics shows that the proposed method is better than some state-of-the-art methods.
Highlights
With the advancement of science and technology, life has become simple and easier
We have proposed an image hashing algorithm that uses some preprocessing steps, discrete wavelet transform (DWT)-Singular value decomposition (SVD) and saliency detection technique using a spectral residual model
6 Conclusions In this paper, we have proposed an image hashing algorithm which is based on DWT-SVD and spectral residual method
Summary
With the advancement of science and technology, life has become simple and easier. Internet and multimedia devices have facilitated in easy clicking, storing and sharing images, videos and songs. The lexicographical framework based on DCT and NMF has been used to generate image hash by Tang et al [20] This method is robust to some content-preserving operation, but variant for rotation of large degree. The hashing technique based on ring partition of an image, and followed by NMF to generate hash, has been proposed by Tang et al [2] This method is robust to digital image manipulations, but limited to forgery of images in corners. Wang et al [26] proposed an image hashing by concatenating the features from image blocks and key points This method is robust to some digital image manipulation, identifies the forged area, but sensitive for large angle rotation. This function has been used to calculate the RT (Rf (p, ∅)) with the limit of 0 ≤ ∅ ≤ 2π
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