With the development of deepfake methods, a large number of deepfake images and videos have been widely disseminated on the internet, raising public concerns about the authenticity of information. Therefore, deepfake detection has recently become a hot topic in the field of computer vision, and many methods have been proposed. Currently, frequency-based detection methods have achieved commendable results, but there are still two issues: a) These methods use fixed filters to focus on fixed frequency bands and areas, making them easily distracted by irrelevant information and lacking flexibility for different forgery methods. b) The methods that fuse frequency domain information with RGB information using CNNs do not consider global relationships, so they are insufficient to fully utilize both types of information. To address these issues, we introduce a Frequency-Enhanced Transformer Network (FETNet). Specifically, we propose a Frequency Feature Enhancement Module (FFEM), which is a learnable module capable of flexibly enhancing important frequency bands and regions in the original frequency features. Additionally, we present a Feature Fusion Transformer (FFT) that considers global information to fuse features from the RGB and frequency domains, achieving a more comprehensive feature representation. Through extensive experiments on the FF++ dataset, the effectiveness and superiority of our approach have been demonstrated.
KEYWORDS: Counterfeit detection, Image enhancement, Data modeling, Education and training, Feature extraction, Visualization, Detection and tracking algorithms, RGB color model, Performance modeling, Visual process modeling
In real life, image forgery techniques such as stitching, copying, moving, deleting, and enhancing operations are rampant, causing serious social harm. We propose an image forgery detection called LaTe-Net. LaTe-Net does not require additional preprocessing or postprocessing and can detect images of any size and image forgery operation type at the same time. In this paper, we designed a texture enhancement module that focuses on local texture features and amplifies subtle pseudo shadows in shallow features to detect forged images by identifying local abnormal features. In addition, we perform forgery localization through local abnormality detection to capture local abnormalities. Finally, we conducted comparative and visual experiments on different public datasets, and the experimental results demonstrate that LaTe-Net has good performance and generalization properties for different types of forgery operations.
In recent years, speech synthesis technology has become increasingly advanced, leading to a proliferation of forged audio content on the internet, which poses significant threat to individuals and society. Many studies have utilized a range of deep learning-based techniques to differentiate fake audio content, but the features used in these studies are often limited in their rich and generalizable characteristics. In this paper, we propose a novel fake voice detection technology that utilizes the wav2vec2 model for feature extraction along with a custom-designed residual-based detection module to augment the detection of fake audio content with greater accuracy and precision. Additionally, we incorporate a data augmentation method to improve the performance of the model and enhance its ability to generalize. We trained our model on the ASVspoof2019 dataset and evaluated it on the LA and DF datasets of the ASVspoof2021 dataset. Supplementary experiments demonstrated that our approach achieved state-of-the-art detection performance and illustrated its effectiveness and applicability.
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