Although face recognition starts to play an important role in our daily life, we need to pay attention that data-driven face recognition vision systems are vulnerable to adversarial attacks. However, current digital adversarial attacks and physical adversarial attacks both have drawbacks, with the former ones impractical and the latter one conspicuous, high-computational and low-executable. To address the issues, we propose a practical, executable, stealthy and low computational adversarial attack based on LED illumination modulation. To fool the systems, the proposed attack generates physically imperceptible luminance changes to human eyes through fast intensity modulation of scene LED illumination and uses the rolling shutter effect of CMOS image sensors in face recognition systems to implant luminance information perturbation to the captured face images. In summary, we present a denial-of-service (DoS) attack for face detection and an evasion attack for face verification. We also evaluate their effectiveness against wellknown face detection models, Dlib, MTCNN and RetinaFace, and face verification models, Dlib, FaceNet, and ArcFace. The extensive physical experiments show that the success rates of DoS attacks against face detection models reach 97.67%, 100%, and 100%, respectively, and the success rates of evasion attacks against all face verification models reach 100%.

To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (OCIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, all continual learning models face a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose an Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. By utilizing a new weight function, the revised focal loss will pay more attention to current ambiguous samples, which are the potentially valuable samples to make model progress quickly. To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for OCIL have shown the effectiveness of AFS. The code will be available at https://github.com/czjghost/AFS.

Non-exemplar class-incremental learning refers to continual classifying of new and old classes without storing samples of old classes. Since only new class samples are available, catastrophic forgetting of old knowledge often occurs. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Features. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied to inputs to generate augmented samples and extra class labels. By extending the data and label space, the model can learn more diverse and transferable representations, which can prevent the model from being biased towards learning task-specific features and facilitate the transfer among different tasks. In a word, when learning new tasks, the random auxiliary class augmentation will reduce the change of feature space and improve model generalization. On the other hand, we propose to replace the new features with mixed features for model optimization since only using new features will largely affect the previous representation embedded in the old feature space. Instead, by mixing new and old features, the cosine similarity is improved by reducing the angle between the current and old features, which allows for better stability over long-term incremental learning without increasing the computational complexity. We have conducted extensive experiments on three benchmarks CIFAR-100, Tiny-ImageNet and ImageNet-Subset, where our method outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this article proposes a value entropy model that links the operating state of the system with the efficiency of value creation, which helps to clarify the performance of the service ecosystem from the perspective of multi-dimensional integration. In addition, a computational experiment system is established to verify the effectiveness of value entropy model, which stimulates the competitive evolution process of two service ecosystems with different strategies. The result shows that our model can provide new ideas for the analysis of service ecosystem evolution, and can also provide decision support for the optimization of operation strategy.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. With the increasing complexity of collaborative relationships between services, service ecosystems are beginning to emerge with the characteristics of natural ecosystems, economic systems and complex networks. Under this context, how to realize systematic evaluation of service ecosystem is of great significance to promote its sound development. As shown in Fig.1 , the value creation of service ecosystem consists of three elements: Input, Output, and Operation. Input means customers’ value demands, which drives the constant evolution of service ecosystem. Output means the value created by the service ecosystem in a certain period of time. Operation means the value creation ability of service ecosystem.

With the development of cloud computing, service computing, IoT(Internet of Things) and mobile Internet, the diversity and sociality of services are increasingly apparent. To meet the customized user demands, service ecosystems begins to emerge with the formation of various IT services collaboration network. However, service ecosystem is a complex social-technology system with the characteristics of natural ecosystems, economic systems and complex networks. Hence, how to realize the multi-dimensional evaluation of service ecosystem is of great significance to promote its sound development. Based on this, this paper proposes a value entropy model to analyze the performance of service ecosystem, which is conducive to integrate evaluation indicators of different dimensions. In addition, a computational experiment system is constructed to verify the effectiveness of value entropy model. The result shows that our model can provide new means and ideas for the analysis of service ecosystem.

Autonomous driving systems have a rich and diverse set of sensors and collect a tremendous amount of data during their operations. This has significant implications for individual privacy and induces a new type of potential privacy risks - collateral privacy risks. It is important for the public and the developer community to be aware of the collateral privacy risk posed by current autonomous driving software systems. We performed data privacy analysis for the Apollo project, an open-source autonomous driving software system. We applied source code-based privacy auditing techniques tailored for this particular problem and produced preliminary results, although there were unresolved open issues remaining. As we performed auditing, Apollo was upgraded from version 3.0 to 3.5 with significant under-the-hood technology changes. It was a challenge to perform the analysis as the underlying software evolves and maintain a result that is up-to-date. To address this challenge, we developed and deployed a continuous source code privacy risk analysis tool to assist in the process. In this paper, we discuss our experience and lessons learned from this industrial case study.