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Abstract
In order to make full use of image flipping information to get comprehensive image features and improve the distinguishing performance of hash algorithm, this paper proposes a new image hashing algorithm based on mirror flipping and a three‐dimensional space angle. Firstly, the original image is preprocessed and then combined with mirror flipping image to obtain the new luminance component and opposite color components. Then, we combine new luminance component with the different sizes of structural elements to construct morphological features. The new opposite color components are used to construct a three‐dimensional space. The angle between vectors formed by the pixels in the three‐dimensional space is computed to construct the space angle features. Finally, the morphological features and space angle features are combined and disturbed to form the final hash sequence. Simulation results show that the algorithm has good security and tamper image recognition accuracy. Compared with some existing algorithms, it has better image classification performance and shorter computation time.
Highlights
With the rapid development of Internet technology, multimedia information security is becoming more and more important in people’s life
We propose an image hashing algorithm based on the mirror flipping and a threedimensional space angle
This work proposes an image hashing algorithm based on mirror flipping and space angle
Summary
With the rapid development of Internet technology, multimedia information security is becoming more and more important in people’s life. Image hashing is an important part of multimedia information security. The key security refers to the failure of an attacker to obtain the correct hash sequence without knowing the correct key. These features enable the image hashing to be applied to image retrieval, image classification, and image tamper detection. We combine the image flipping of the image component with the local features of the three-dimensional space to construct the hash sequence. This method makes full improvement of the classification performance of hashing algorithm
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