It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a...It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a content-based image hashing scheme using wave atoms is proposed, which satisfies the above criteria. Compared with traditional transforms like wavelet transform and discrete cosine transform （DCT）, wave atom transform is adopted for the sparser expansion and better characteristics of texture feature extraction which shows better performance in both robustness and fragility. In addition, multi-frequency detection is presented to provide an application-defined trade-off. To ensure the security of the proposed approach and its resistance to a chosen-plaintext attack, a randomized pixel modulation based on the Rdnyi chaotic map is employed, combining with the nonliner wave atom transform. The experimental results reveal that the proposed scheme is robust against content-preserving manipulations and has a good discriminative capability to malicions tampering.展开更多
A lexicographic image hash method based on space and frequency features was proposed. At first, the image database was constructed, and then color and texture features were extracted from the image blocks including in...A lexicographic image hash method based on space and frequency features was proposed. At first, the image database was constructed, and then color and texture features were extracted from the image blocks including information for every image in the database, which formed feature vectors. The feature vectors were clustered to form dictionary. In hash generation, the image was preproc^ssed and divided into blocks firstly. Then color and texture features vectors were extracted from the blocks. These feature vectors were used to search the dictionary, and the nearest word in dictionary for each block was used to form the space features. At the same time. frequency feature was extracted from each block. The space and frequency features were connected to form the intermediate hash. Lastly, the final hash sequence was obtained by pseudo-randomly permuting the intermediate hash. Experiments show that the method has a very low probability of collision and a good perception of robustness. Compared with other methods, this method has a low collision rate.展开更多
文摘It is well known that robustness, fragility, and security are three important criteria of image hashing; however how to build a system that can strongly meet these three criteria is still a challenge. In this paper, a content-based image hashing scheme using wave atoms is proposed, which satisfies the above criteria. Compared with traditional transforms like wavelet transform and discrete cosine transform （DCT）, wave atom transform is adopted for the sparser expansion and better characteristics of texture feature extraction which shows better performance in both robustness and fragility. In addition, multi-frequency detection is presented to provide an application-defined trade-off. To ensure the security of the proposed approach and its resistance to a chosen-plaintext attack, a randomized pixel modulation based on the Rdnyi chaotic map is employed, combining with the nonliner wave atom transform. The experimental results reveal that the proposed scheme is robust against content-preserving manipulations and has a good discriminative capability to malicions tampering.
基金Natural Science Foundations of Shanghai,China(Nos.15ZR1418500,15ZR1418400)the Training Program of Shanghai University of Electric Power for Academic Backbone Teachers,China
文摘A lexicographic image hash method based on space and frequency features was proposed. At first, the image database was constructed, and then color and texture features were extracted from the image blocks including information for every image in the database, which formed feature vectors. The feature vectors were clustered to form dictionary. In hash generation, the image was preproc^ssed and divided into blocks firstly. Then color and texture features vectors were extracted from the blocks. These feature vectors were used to search the dictionary, and the nearest word in dictionary for each block was used to form the space features. At the same time. frequency feature was extracted from each block. The space and frequency features were connected to form the intermediate hash. Lastly, the final hash sequence was obtained by pseudo-randomly permuting the intermediate hash. Experiments show that the method has a very low probability of collision and a good perception of robustness. Compared with other methods, this method has a low collision rate.
