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With the development of deep learning technologies, video face tampering technologies, represented by Deepfake, can easily generate fake face images of videos by modifying the original video with only a small amount of face images. Therefore, the detection of forged facial videos has become critical to internet content regulation. In this paper, a deep forgery face video detection method with fusion of frequency domain features and spatial domain features (FFS) is proposed to address the problem of deepfake face video detection. At first, the proposed method extracts the wavelet features of images with two-dimensional discrete wavelet transform, then extracts the multidimensional wavelet feature vectors of images according to n-level wavelet decomposition. The frequency domain features of the image extracted by the discrete Fourier transform are also cascaded and fused with the wavelet features. Besides, the proposed method can better address the overfitting problem of detection methods in practical internet application scenarios by establishing a shared updatable strategy. Finally, in order to improve the generalization ability of the detection model and address the problem that the model is vulnerable to malicious attacks under the above shared update strategy, blockchain technology is employed to implement an incentive mechanism. It can motivate participants to provide real and health data, and then achieve the establishment and maintenance of a good shared and renewable environment. Moreover, we use DeepfakeDetection and Celeb-DF datasets to conduct the experiments. Samples with different percentages of high-quality images are selected to simulate the complex environment of image quality in the Internet. Experimental results show that the proposed method can improve the performance of the face forgery detection model effectively. [ABSTRACT FROM AUTHOR]

E-commerce has developed greatly in recent years, as such, its regulations have become one of the most important research areas in order to implement a sustainable market. The analysis of a large amount of reviews data generated in the shopping process can be used to facilitate regulation: since the review data is short text and it is easy to extract the features through deep learning methods. Through these features, the sentiment analysis of the review data can be carried out to obtain the users' emotional tendency for a specific product. Regulators can formulate reasonable regulation strategies based on the analysis results. However, the data has many issues such as poor reliability and easy tampering at present, which greatly affects the outcome and can lead regulators to make some unreasonable regulatory decisions according to these results. Blockchain provides the possibility of solving these problems due to its trustfulness, transparency and unmodifiable features. Based on these, the blockchain can be applied for data storage, and the Long short-term memory (LSTM) network can be employed to mine reviews data for emotional tendencies analysis. In order to improve the accuracy of the results, we designed a method to make LSTM better understand text data such as reviews containing idioms. In order to prove the effectiveness of the proposed method, different experiments were used for verification, with all results showing that the proposed method can achieve a good outcome in the sentiment analysis leading to regulators making better decisions. [ABSTRACT FROM AUTHOR]

As a part of food safety research, researches on food transactions safety has attracted increasing attention recently. Food choice is an important factor affecting food transactions safety: It can reflect consumer preferences and provide a basis for market regulation. Therefore, this paper proposes a food market regulation method based on blockchain and a deep learning model: Stacked autoencoders (SAEs). Blockchain is used to ensure the fairness of transactions and achieve transparency within the transaction process, thereby reducing the complexity of the trading environment. In order to enhance the usability, relevant Web pages have been developed to make it more friendly and conduct a security analysis for using blockchain. Consumers' reviews after the transactions are finished can be used to train SAEs in order to perform emotional tendencies predictions. Compared with different advanced models for predictions, the test results show that SAEs have a better performance. Furthermore, in order to provide a basis for the formulation of regulation strategies and its related policies, case studies of different traders and commodities have also been conducted, proving the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]