Jun-Teng Yang

Jun-Teng Yang, Wen-Yuan Chen, Che-Hua Li et al.

Recent research in Internet of things has been widely applied for industrial practices, fostering the exponential growth of data and connected devices. Henceforth, data-driven AI models would be accessed by different parties through certain data-sharing policies. However, most of the current training procedures rely on the centralized data-collection strategy and a single computational server. However, such a centralized scheme may lead to many issues. Customer data stored in a centralized database may be tampered with so the provenance and authenticity of data cannot be justified. Once the aforementioned security concerns occur, the credibility of the trained AI models would be questionable and even unfavorable outcomes might be produced at the test stage. Lately, blockchain and AI, the two core technologies in Industry 4.0 and Web 3.0, have been explored to facilitate the decentralized AI training strategy. To serve on this very purpose, we propose a new system architecture called APPFLChain, namely an integrated architecture of a Hyperledger Fabric-based blockchain and a federated-learning paradigm. Our proposed new system allows different parties to jointly train AI models and their customers or stakeholders are connected by a consortium blockchain-based network. Our new system can maintain a high degree of security and privacy as users do not need to share sensitive personal information to the server. For numerical evaluation, we simulate a real-world scenario to illustrate the whole operational process of APPFLChain. Simulation results show that taking advantage of the characteristics of consortium blockchain and federated learning, APPFLChain can demonstrate favorable properties including untamperability, traceability, privacy protection, and reliable decision-making.

Jun-Teng Yang, Sheng-Che Kao, Scott C. -H. Huang

With the improvement of AI chips (e.g., GPU, TPU, and NPU) and the fast development of the Internet of Things (IoT), some robust deep neural networks (DNNs) are usually composed of millions or even hundreds of millions of parameters. Such a large model may not be suitable for directly deploying on low computation and low capacity units (e.g., edge devices). Knowledge distillation (KD) has recently been recognized as a powerful model compression method to decrease the model parameters effectively. The central concept of KD is to extract useful information from the feature maps of a large model (i.e., teacher model) as a reference to successfully train a small model (i.e., student model) in which the model size is much smaller than the teacher one. Although many KD methods have been proposed to utilize the information from the feature maps of intermediate layers in the teacher model, most did not consider the similarity of feature maps between the teacher model and the student model. As a result, it may make the student model learn useless information. Inspired by the attention mechanism, we propose a novel KD method called representative teacher key (RTK) that not only considers the similarity of feature maps but also filters out the useless information to improve the performance of the target student model. In the experiments, we validate our proposed method with several backbone networks (e.g., ResNet and WideResNet) and datasets (e.g., CIFAR10, CIFAR100, SVHN, and CINIC10). The results show that our proposed RTK can effectively improve the classification accuracy of the state-of-the-art attention-based KD method.

Jun-Teng Yang, Guei-Ming Liu, Scott C. -H Huang

Deception detection is an important task that has been a hot research topic due to its potential applications. It can be applied in many areas, from national security (e.g., airport security, jurisprudence, and law enforcement) to real-life applications (e.g., business and computer vision). However, some critical problems still exist and are worth more investigation. One of the significant challenges in the deception detection tasks is the data scarcity problem. Until now, only one multi-modal benchmark open dataset for human deception detection has been released, which contains 121 video clips for deception detection (i.e., 61 for deceptive class and 60 for truthful class). Such an amount of data is hard to drive deep neural network-based methods. Hence, those existing models often suffer from overfitting problems and low generalization ability. Moreover, the ground truth data contains some unusable frames for many factors. However, most of the literature did not pay attention to these problems. Therefore, in this paper, we design a series of data preprocessing methods to deal with the aforementioned problem first. Then, we propose a multi-modal deception detection framework to construct our novel emotional state-based feature and use the open toolkit openSMILE to extract the features from the audio modality. We also design a voting scheme to combine the emotional states information obtained from visual and audio modalities. Finally, we can determine the novel emotion state transformation feature with our self-designed algorithms. In the experiment, we conduct the critical analysis and comparison of the proposed methods with the state-of-the-art multi-modal deception detection methods. The experimental results show that the overall performance of multi-modal deception detection has a significant improvement in the accuracy from 87.77% to 92.78% and the ROC-AUC from 0.9221 to 0.9265.