The emergence of multi-server authentication key protocol schemes provides a viable environment for users to easily access the services of multiple legitimate servers through a single registration. Biometric identification technology has the characteristics of forgery difficulty, duplication difficulty and guess difficulty, etc. Therefore, it is an indispensable authentication technology in smart card-based user authentication protocol. There are many shortcomings in the existing schemes based on biometrics, including leakages of biometrics information, smart card theft attack, lack of user anonymity, user impersonation attack, server impersonation, and so on. To overcome these shortcomings, we propose a new user authentication and key agreement scheme in the multi-server environment. To some extent, we not only are able to guarantee the communication security between the user and the servers, but also ensure the physical security of the smart card and biometrics information. In this respect, we use lightweight cryptographic primitives, such as Physically Unclonable Functions (PUFs), Fuzzy extractor and One-way hash functions, and so on. The proposed scheme can effectively protect user’s anonymity without the use of password and provide mutual authentication and key agreement in the multi-server environment. Subsequently, we used informal analysis, Burrows-Abadi-Needham Logic (BAN-Logic) proof, and a widely accepted Real-Or-Random model to prove the security and robustness of proposed scheme. Finally, our authentication protocol can protect the security of communication.

Cerenkov luminescence tomography (CLT) was developed to reconstruct a three-dimensional (3D) distribution of radioactive probes inside a living animal. Reconstruction methods are generally performed within a unique framework by searching for the optimum solution. However, the ill-posed aspect of the inverse problem usually results in the reconstruction being non-robust. In addition, the reconstructed result may not match reality since the difference between the highest and lowest uptakes of the resulting radiotracers may be considerably large, therefore the biological significance is lost. In this paper, based on the minimization of a conformance error, a probability method is proposed that consists of qualitative and quantitative modules. The proposed method first pinpoints the organ that contains the light source. Next, we developed a 0-1 linear optimization subject to a space constraint to model the CLT inverse problem, which was transformed into a forward problem by employing a region growing method to solve the optimization. After running through all of the elements used to grow the sources, a source sequence was obtained. Finally, the probability of each discrete node being the light source inside the organ was reconstructed. One numerical study and two in vivo experiments were conducted to verify the performance of the proposed algorithm, and comparisons were carried out using the hp-finite element method (hp-FEM). The results suggested that our proposed probability method was more robust and reasonable than hp-FEM.

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision.

Deep learning has been used in many computer-vision-based industrial Internet of Things applications. However, deep neural networks are vulnerable to adversarial examples that have been crafted specifically to fool a system while being imperceptible to humans. In this article, we propose a consensus defense (Cons-Def) method to defend against adversarial attacks. Cons-Def implements classification and detection based on the consensus of the classifications of the augmented examples, which are generated based on an individually implemented intensity exchange on the red, green, and blue components of the input image. We train a CNN using augmented examples together with their original examples. For the test image to be assigned to a specific class, the class occurrence of the classifications on its augmented images should be the maximum and reach a defined threshold. Otherwise, it is detected as an adversarial example. The comparison experiments are implemented on MNIST, CIFAR-10, and ImageNet. The average defense success rate (DSR) against white-box attacks on the test sets of the three datasets is 80.3%. The average DSR against black-box attacks on CIFAR-10 is 91.4%. The average classification accuracies of Cons-Def on benign examples of the three datasets are 98.0%, 78.3%, and 66.1%. The experimental results show that Cons-Def shows a high classification performance on benign examples and is robust against white-box and black-box adversarial attacks.