In lightweight cryptographic primitives, round functions with only simple operations XOR, modular addition and rotation are widely used nowadays. This kind of ciphers is called ARX ciphers. For ARX ciphers, impossible...In lightweight cryptographic primitives, round functions with only simple operations XOR, modular addition and rotation are widely used nowadays. This kind of ciphers is called ARX ciphers. For ARX ciphers, impossible differential cryptanalysis and zero-correlation linear cryptanalysis are among the most powerful attacks, and the key problems for these two attacks are discovering more and longer impossible differentials(IDs) and zero-correlation linear hulls(ZCLHs). However, finding new IDs and ZCLHs for ARX ciphers has been a manual work for a long time, which has been an obstacle in improving these two attacks. This paper proposes an automatic search method to improve the efficiency of finding new IDs and ZCLHs for ARX ciphers. In order to prove the efficiency of this new tool, we take HIGHT, LEA, SPECK three typical ARX algorithms as examples to explore their longer and new impossible differentials and zero-correlation linear hulls. To the best of our knowledge, this is the first application of automatic search method for ARX ciphers on finding new IDs and ZCLHs. For HIGHT, we find more 17 round IDs and multiple 17 round ZCLHs. This is the first discovery of 17 round ZCLHs for HIGHT. For LEA, we find extra four 10 round IDs and several 9 round ZCLHs. In the specification of LEA, the designers just identified three 10 round IDs and one 7round ZCLH. For SPECK, we find thousands of 6 round IDs and forty-four 6 round ZCLHs. Neither IDs nor ZCLHs of SPECK has been proposed before. The successful application of our new tool shows great potential in improving the impossible differential cryptanalysis and zero-correlation linear cryptanalysis on ARX ciphers..展开更多
It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） re...It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder＇s LRBs adapt to the external parameter of the decoder like SNR （Signal Noise Ratio） level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah＇s iterative decoding algorithm using the fixed LRBs parameter.展开更多
Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntact...Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61572516, 61402523, 61202491, 61272041 and 61272488
文摘In lightweight cryptographic primitives, round functions with only simple operations XOR, modular addition and rotation are widely used nowadays. This kind of ciphers is called ARX ciphers. For ARX ciphers, impossible differential cryptanalysis and zero-correlation linear cryptanalysis are among the most powerful attacks, and the key problems for these two attacks are discovering more and longer impossible differentials(IDs) and zero-correlation linear hulls(ZCLHs). However, finding new IDs and ZCLHs for ARX ciphers has been a manual work for a long time, which has been an obstacle in improving these two attacks. This paper proposes an automatic search method to improve the efficiency of finding new IDs and ZCLHs for ARX ciphers. In order to prove the efficiency of this new tool, we take HIGHT, LEA, SPECK three typical ARX algorithms as examples to explore their longer and new impossible differentials and zero-correlation linear hulls. To the best of our knowledge, this is the first application of automatic search method for ARX ciphers on finding new IDs and ZCLHs. For HIGHT, we find more 17 round IDs and multiple 17 round ZCLHs. This is the first discovery of 17 round ZCLHs for HIGHT. For LEA, we find extra four 10 round IDs and several 9 round ZCLHs. In the specification of LEA, the designers just identified three 10 round IDs and one 7round ZCLH. For SPECK, we find thousands of 6 round IDs and forty-four 6 round ZCLHs. Neither IDs nor ZCLHs of SPECK has been proposed before. The successful application of our new tool shows great potential in improving the impossible differential cryptanalysis and zero-correlation linear cryptanalysis on ARX ciphers..
基金the National Natural Science Foundation of China under grants，NUAA research funding
文摘It is known that Block Turbo Codes （BTC） can be nearly optimally decoded by Chase-II algorithm, in which the Least Reliable Bits （LRBs） are chosen empirically to keep the size of the test patterns （sequences） relatively small and to reduce the decoding complexity. While there are also other adaptive techniques, where the decoder＇s LRBs adapt to the external parameter of the decoder like SNR （Signal Noise Ratio） level, a novel adaptive algorithm for BTC based on the statistics of an internal variable of the decoder itself is proposed in this paper. Different from the previous reported results, it collects the statistics of the multiplicity of the candidate sequences, i.e., the number of the same candidate sequences with the same minimum squared Euclidean distance resulted from the decoding of test sequences. It is shown by Monte Carlo simulations that the proposed adaptive algorithm has only about 0.02dB coding loss but the average complexity of the proposed algorithm is about 42% less compared with Pyndiah＇s iterative decoding algorithm using the fixed LRBs parameter.
基金Project supported by the National Natural Science Foundation of China (Nos 60533090 and 60603096)the National High-Tech Research and Development Program (863) of China (No 2006AA 010107)
文摘Automatic web image annotation is a practical and effective way for both web image retrieval and image understanding. However, current annotation techniques make no further investigation of the statement-level syntactic correlation among the annotated words, therefore making it very difficult to render natural language interpretation for images such as "pandas eat bamboo". In this paper, we propose an approach to interpret image semantics through mining the visible and textual information hidden in images. This approach mainly consists of two parts: first the annotated words of target images are ranked according to two factors, namely the visual correlation and the pairwise co-occurrence; then the statement-level syntactic correlation among annotated words is explored and natural language interpretation for the target image is obtained. Experiments conducted on real-world web images show the effectiveness of the proposed approach.
