Within the domain of Artificial Intelligence Generated Content (AIGC), technological strides in image generation have been marked, resulting in the proliferation of deepfake images that pose substantial security threats. The current landscape of deepfake detection technologies is marred by limited generalization across diverse generative models and a subpar detection rate for images generated through diffusion processes. In response to these challenges, this paper introduces a novel detection model designed for high generalizability, leveraging multiscale frequency and spatial domain features. Our model harnesses an array of specialized filters to extract frequency‐domain characteristics, which are then integrated with spatial‐domain features captured by a Feature Pyramid Network (FPN). The integration of the Attentional Feature Fusion (AFF) mechanism within the feature fusion module allows for the optimal utilization of the extracted features, thereby enhancing detection capabilities. We curated an extensive dataset encompassing deepfake images from a variety of GANs and diffusion models for rigorous evaluation. The experimental findings reveal that our proposed model achieves superior accuracy and generalization compared to existing baseline models when confronted with deepfake images from multiple generative sources. Notably, in cross‐model detection scenarios, our model outperforms the next best model by a significant margin of 29.1% for diffusion‐generated images and 15.1% for GAN‐generated images. This accomplishment presents a viable solution to the pressing issues of generalization and adaptability in the field of deepfake detection.

In this paper we propose an attack method to embed digital watermarking invisibly into a clean example to generate an adversarial example to interfere with the classification of deep learning models. Specifically, we propose an optimization algorithm called Non-Dominated Sorting Genetic Algorithm with Particle Swarm Optimization (NSGA-PSO) to generate adversarial digital watermarking in the black-box attack mode with a few queries from the models to be attacked. Extensive experiments on ImageNet and CIFAR-10 datasets demonstrate that our method can efficiently generate adversarial examples with higher attack success rates than existing black-box attack methods. Furthermore, showing satisfactory transferability across different network models and greater robustness against image transformation defense methods.

In this study, to achieve the possibility of predicting face by skull automatically, we propose a craniofacial reconstruction method based on the end-to-end deep convolutional neural network. Three-dimensional volume data are obtained from 1447 head CT scans of Chinese people of different ages. The facial and skull surface data are projected onto two-dimensional space to generate a two-dimensional elevation map, and then, use the deep convolution neural network to realize the prediction of skull to face shape in two-dimensional space. The encoder and decoder are composed of first feature extraction through the encoder and then as the input of the decoder to generate the craniofacial restoration image. In order to accurately describe the features of different scales, we adopt an U-shaped codec structure with cross-layer connections. Therefore, the output features are decomposed with the features of the corresponding scales in the encoding stage to achieve the integration of different scales while restoring the feature scales in the compression and decoding stage. Meanwhile, the U-net structures help to avoid the problem of loss of detail features in the downsampling process. We use supervised learning to obtain the prediction model from skull to facial elevation map. Back-projection operation is performed afterwards to generate facial surface data in 3D space. Experiments show that the proposed method in this study can effectively achieve craniofacial reconstruction, and for most part of the face, restoration error is controlled within 2 mm.