Using wireless mobile terminals has become the mainstream of Internet transactions, which can verify the identity of users by passwords, fingerprints, sounds, and images. However, once these identity data are stolen, traditional information security methods will not avoid online transaction fraud. The existing convolutional neural network model for fraud detection needs to generate many derivative features. This paper proposes a fraud detection model based on the convolutional neural network in the field of online transactions, which constructs an input feature sequencing layer that implements the reorganization of raw transaction features to form different convolutional patterns. Its significance is that different feature combinations entering the convolution kernel will produce different derivative features. The advantage of this model lies in taking low dimensional and nonderivative online transaction data as the input. The whole network consists of a feature sequencing layer, four convolutional layers and pooling layers, and a fully connected layer. Verifying with online transaction data from a commercial bank, the experimental results show that the model achieves excellent fraud detection performance without derivative features. And its precision can be stabilized at around 91% and recall can be stabilized at around 94%, which increased by 26% and 2%, respectively, comparing with the existing CNN for fraud detection.

Deepfake technology uses artificial intelligence to create realistic but false audio, images, and videos. Deepfake technology poses a significant threat to the authenticity of visual content, particularly in live-stream scenarios where the immediacy of detection is crucial. Existing Deepfake detection approaches have limitations and challenges, prompting the need for more robust and accurate solutions. This research proposes an innovative approach: combining eye movement analysis with a hybrid deep learning model to address the need for real-time Deepfake detection. The proposed hybrid deep learning model integrates two deep neural network architectures, MesoNet4 and ResNet101, to leverage their respective architectures’ strengths for effective Deepfake classification. MesoNet4 is a lightweight CNN model designed explicitly to detect subtle manipulations in facial images. At the same time, ResNet101 handles complex visual data and robust feature extraction. Combining the localized feature learning of MesoNet4 with the deeper, more comprehensive feature representations of ResNet101, our robust hybrid model achieves enhanced performance in distinguishing between manipulated and authentic videos, which cannot be conducted with the naked eye or traditional methods. The model is evaluated on diverse datasets, including FaceForensics++, CelebV1, and CelebV2, demonstrating compelling accuracy results, with the hybrid model attaining an accuracy of 0.9873 on FaceForensics++, 0.9689 on CelebV1, and 0.9790 on CelebV2, showcasing its robustness and potential for real-world deployment in content integrity verification and video forensics applications.

Cloud Computing is becoming more and more popular for solving problems that need high concurrency and a lot of resources. Many traditional areas of research choose to solve their problems through the cloud, and workflow scheduling is one of them. Cloud computing brings many benefits, meanwhile, due to the almost “infinite” amount of resources for users, it also brings new challenges for scheduling and optimization, in which cost and makespan are the most concerned issues for workflow scheduling. Users want to obtain a low cost and fast makespan solution. This paper focuses on how to find an optimized solution to achieve better cost-makespan at the same time under the constraint of deadline. In order to solve this problem, an immune particle swarm optimization algorithm (IMPSO) is proposed, which effectively improves the quality and speed of the optimization. The proposed IMPSO overcomes the problem of slow convergence of PSO, which is easy to fall into local optimization. Experiments show the efficiency and effectiveness of the proposed approach.

Deepfake detection has become a critical challenge nowadays with the rise of sophisticated generative techniques that manipulate audio-visual data. Existing methods primarily focus on lip movement synchronization using audio and visual features, often relying on local feature extraction with Convolutional Neural Networks (CNNs). In this work, we propose an enhanced multimodal framework that integrates with local and global features for advanced deepfake detection. Our approach extends traditional pipelines by introducing additional visual features such as eye movement and facial regions, combined with audio features to model cross-modal dependencies. While CNNs capture local features, Vision Transformers (ViTs) extract global contextual relationships from both visual and audio modalities. The diffusion models are incorporated as pre-processors to refine noisy data and generate realistic augmentations, ensuring high-quality feature representation. The proposed framework achieves state-of-the-art performance, with accuracy scores of 0.9987, 0.9825, 0.9915, and 0.9812 on the FakeAVCeleb, AV-Deepfake1M, TVIL, and LAV-DF datasets, respectively. These results demonstrate significant improvements over existing methods, highlighting the framework’s superior generalization and robustness in detecting subtle inconsistencies across manipulated audio-visual data.

The rapid advancements in Artificial Intelligence (AI) and deep learning techniques have led to the creation of highly realistic synthetic media known as deepfakes. These manipulated images, videos, and audio pose significant ethical, security, and privacy concerns. To address this issue, we propose a novel Audio–Visual Synchronisation and Fusion Framework (AVSFF) for real-time detection of deepfakes. This approach focuses on fine-grained lip movement analysis by detecting subtle inconsistencies between lip movements and corresponding audio. By integrating visual and audio features using multimodal fusion techniques, AVSFF aims to distinguish between authentic and manipulated media. The proposed framework is evaluated on diverse datasets, including FakeAVCeleb, AV-Deepfake1M, TVIL, and LAV-DF, demonstrating promising results with accuracies of 0.9973, 0.9760, 0.9890, and 0.9786, respectively. This study contributes to the field by providing a robust real-time solution for detecting deepfakes in audio–visual synthetic data, ensuring enhanced detection accuracy and effective generalization across various deepfake manipulations and demographic data.

The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate. So we propose a new method for individual behavior construction, which can make the behavior of lowfrequency users more accurate by migrating the current transaction group behavior and transaction status. Firstly, we consider the user’s only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user’s own transaction behavior benchmark. Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group. Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism. The combination of the three constitutes a new transaction behavior of the user. On this basis, a multi-behavior detection model based on new transaction behavior is proposed. According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud. Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions.