Abstract White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to protect the key. However, with the development of white-box attack techniques, many white-box implementations have been proved to be insecure. In this paper, a new white-box implementation of SM4 is proposed, which is based on an equivalent partial SPN structure of the SM4 algorithm. Our implementation includes three types of table lookup operations and XOR operations. The round keys are obfuscated with the self-equivalences of the S-box and random affine encodings. Security analysis shows that our implementation can resist BGE-type attack, the attack based on affine equivalence algorithm, the structure attack, the collision attack and differential computational analysis. Furthermore, our scheme requires 8.125 MB of memory.

An artistic line portrait robot can generate, process, and draw line portraits. Compared to real face images, line portraits lose some recognizable information. Maintaining recognizability during the process of expression edition of line portraits is an important challenge for artistic portrait robots. A recognizable expression line portrait synthesis method based on a triangle coordinate system (TCS) is proposed. First, based on public facial expression databases [JAFFE, Oulu CASIA, RaFD, and Cohn-Kanade (CK)], by studying the feature deviations between different expressions of the same person, an expression deformation constraint criterion (EDCC) that is conducive to maintaining recognizable features is proposed. Then, by comparing features between the source line portrait and reference expression portrait, the expression features are calculated. Finally, under the EDCC, based on expression features, a recognizable expression line portrait is generated through image topological deformation based on TCS. In addition, we can synthesize different degrees of expression line portraits. On the public face datasets (FHHQ, CelebA-HQ, and CK), we implemented qualitative and quantitative contrast experiments. Experimental results demonstrate that this method can automatically synthesize an expression line portrait with reference expression, where the expression degree of the reference expression is controllable, and the generated expression portrait still has high recognizability. The expression samples generated by the proposed method are used for face authentication on the CK dataset, and only 0.22% of the samples fail to pass the authentication.

Abstract FOX block cipher was designed with a Lai–Massey scheme, in which the round function uses the Substitution-Permutation-Substitution structure. A meet-in-the-middle (MITM) attack is one of the most important issues for the security of the block cipher, which consists of a precomputation phase for constructing a distinguisher and an online phase for key recovery. This paper studies the MITM attacks against FOX. The first MITM distinguishers of 5-round FOX64, 7-round FOX64-256 and 5-round FOX128 are presented when using the differential enumeration technique with truncated differential characteristics. Then, based on these distinguishers, the attacks for key recovery on 7-round FOX64, 11-round FOX64-256 and 7-round FOX128 are presented with the state-test and state-search techniques. It is shown that the attack on 11-round FOX64-256 is proposed for the first time; attacks on 7-round FOX64 and 7-round FOX128 can be improved with lower time and memory complexities compared with the currently known attacks.

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

Abstract Image quality assessment is to simulate subjective human visual perception and realize image quality inference automatically. Although deep neural networks have achieved great success, the majority of them do not fully consider perception characteristics. Therefore, according to the human visual scale characteristics, we proposed an image quality assessment algorithm based on multiscale and dual domains fusion. Firstly, the original image and its phase congruency respectively input into two branches, feature pyramid and channel attention mechanism are adopted to extract multiscale features. After that, bilinear pool is used to aggregate the spatial and frequency domain characteristics of the corresponding scales, and allows arbitrary scale input to ensure that the features are extracted from the inherent quality images. Finally, the single quality score is obtained through learned weights of each scale. Comparative experiments between our approach and state‐of‐the‐art are conducted on five public databases, the results demonstrate that the proposed algorithm is not only robust to different types and across database, but also sensitive to scale.

Summary In recent years, with the rapid development of adversarial learning technology, facial attribute editing has made great success in a number of areas. Realistic visual effect, invariant identity information, and accurate editing area are the three key issues of facial attribute editing. Unfortunately, most researches focus on the former two problems. However, lack of awareness of the accurate editing area in the task is the main reason for damaging attribute‐irrelevant details. To address this issue, this article proposes a novel facial attribute editing algorithm—a generative adversarial network (GAN) with semantic masks—from the perspective of editing location accuracy. By generating the mask with respect to attribute‐related areas, the semantic segmentation network can only constrain the manipulation in the target region while not harming any attribute‐irrelevant details. The GAN is then combined with the semantic segmentation network to formulate the entire framework, which is referred to as SM‐GAN. Extensive experiments on the public datasets CelebA and LFWA prove that the presented method can not only ensure that the attribute manipulation is realistic, but also allow attribute‐irrelevant regions to remain unchanged. Moreover, it can also simultaneously edit multiple facial attributes.

Abstract Age is one of the most important biological characteristics of the human face. The increase of age coincides with the increase of the aging degree of the face. Face aging synthesis is attracting increasingly more attention from domestic and overseas scholars in the computer vision and computer graphics fields, and it can be integrated into the basic research of face correlation, such as cross‐age face analysis and age estimation. At present, some achievements have been made in face aging synthesis research; however, it is still an urgent problem to reduce the number of parameters and computational complexity of the network while ensuring the aging effect. Therefore, a new face aging algorithm is proposed in this article. Unlike the previous methods of aging process simulation, we introduce an assisted age classification network based on the principle of homology continuity, which is more in line with the human cognition process. After pretraining, the result of age classification is improved, and the pretraining model is then added to the framework of aging face generation for fine‐tuning to constrain the generated aging face, which can improve the aging accuracy of the generated image. Furthermore, we reconstruct the input face by using the age tag of the input face and the synthesized aging face and maintain the identity invariance in the face aging process by minimizing the reconstruction loss. The experimental results show that the method proposed in this article produces a considerable effect of face aging and significantly reduces the number of parameters and the complexity of computational.

Abstract The first 9‐round meet‐in‐the‐middle (MITM) attack and improved 8‐round impossible differential (ID) attacks on Rijndael‐160 are studied here. For the first 9‐round MITM attack, a new effective attack path is explored by using the generalised δ ‐set and the generalised multiset, which are based on the property that the difference branch number of MixColumns is 5. With this attack path, a 5‐round MITM distinguisher with a technique of the truncated differential characteristic is proposed, and then the attack on 9‐round Rijndael‐160 is performed. For the improved 8‐round ID attacks, to take advantage of the key‐schedule weaknesses for Rijndael‐160 under key sizes of 160 and 256 bits, some new attack paths are found. With these attack paths, the 5‐round IDs are proposed based on the property of MixColumns above, and then the attacks on the 8‐round Rijndael‐160 under key sizes of 160 and 256 bits are performed. When compared with the currently known attacks, the proposed attacks have lower data, time, and memory complexities.

The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

The resolution of the microwave radar coincidence imaging (MRCI) can break the diffraction limit, which has been validated by experiments. However, there is still no theoretical analysis. In this article, the resolution of the MRCI is theoretically analyzed using the orthogonal subspace projection algorithm based on the space spanned by the discrete reference radiation mode. First, a target location estimate (TLE) method using the data from the nonfocusing radar array of the MRCI system is proposed to estimate the target position assisted by the equivalent detection method. The estimation precision of the TLE method is approximately equal to the 3-dB beamwidth of the coherent transmitting radar array with the same aperture; hence, the imaging plane can be obtained. Then, the equivalent internal noise (generated by the error of the target distance estimation, i.e., the location of the imaging plane) is theoretically analyzed. Finally, the imaging resolution threshold of the MRCI system is theoretically analyzed. The relationship between the resolution threshold and the factors (such as the deployment of the transmitting radar array, the distance between the target and the radar array, and the signal-to-noise ratio of the imaging system) is summarized. The proposed estimation method and the analyses of the MRCI system are validated through a set of simulations and experiments.

A super-resolution computational imaging method, called post random modulation radar imaging, based on single-input multiple-output radar detection system is proposed in this letter. In the proposed method, the target is detected in coherent mode using the multilinear frequency modulation signal. The echoes received by the radar elements of the receiving array are recorded, respectively. Then, random modulations of the directional pattern factor of the receiving radar array, which are nonlinear processes, are optimized according to the position and the size of the target that are estimated roughly using the traditional method. Finally, the super-resolution radar imaging is obtained to solve the equation group formed by the data from the nonlinear post random modulation process. The resolution of the proposed imaging method can break the diffraction limit corresponding to the aperture of the receiving radar array. The anti-interference ability of the proposed approach is improved significantly compared with the incoherent imaging method using a multiple-input single-output structure. Experiments are carried out to validate the proposed approach.

Bag-of-features has become very popular in Image classification. Offline codebook learning has to limit the number of training sample concerned with memory, and it influences classification accuracy to some extent. We propose an online sparse learning algorithm, which utilizes the reconstruction error to update the current codebook. It can capture salient properties of images in real-time. Most of image representation approaches in Gabor domain merely utilize magnitude information, and some important phase information is missing. Taking both magnitude and phase response into account, a Local Gabor Magnitude Weighted Phase (LGMWP) descriptor is proposed in this paper. The technique works by dividing the image into local patches, extracting SIFT and LGMWP features to online learn the codebook respectively, implementing spatial pyramid matching (SPM) and binary SVM classifier. The experiment results demonstrate our approach outperforms offline learning with a single type of descriptors.