This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images despite limited training data. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. However, the rapid advancements in synthesis technology have led to specific artifacts for each generation model. Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources. To address this issue, we introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors. Our method forces the detector to continuously focus on high-frequency information, exploiting high-frequency representation of features across spatial and channel dimensions. Additionally, we incorporate a straightforward frequency domain learning module to learn source-agnostic features. It involves convolutional layers applied to both the phase spectrum and amplitude spectrum between the Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (iFFT). Extensive experimentation involving 17 GANs demonstrates the effectiveness of our proposed method, showcasing state-of-the-art performance (+9.8\%) while requiring fewer parameters. The code is available at https://github.com/chuangchuangtan/FreqNet-DeepfakeDetection.

Deep hashing methods have achieved tremendous success in cross-modal retrieval, due to its low storage consumption and fast retrieval speed. Supervised cross-modal hashing methods have achieved substantial advancement by incorporating semantic information. However, to a great extent, supervised methods rely on large-scale labeled cross-modal training data which are laborious to obtain. Moreover, most cross-modal hashing methods only handle two modalities of image and text, without taking the scene of multiple modalities into consideration. In this paper, we propose a novel semi-supervised approach called semi-supervised knowledge distillation for cross-modal hashing (SKDCH) to overcome the above-mentioned challenges, which enables guiding a supervised method using outputs produced by a semi-supervised method for multimodality retrieval. Specifically, we utilize teacher-student optimization to propagate knowledge. Furthermore, we improves triplet ranking loss to better mitigate the heterogeneity gap, which increases the discriminability of our proposed approach. Extensive experiments executed on two benchmark datasets validate that the proposed SKDCH surpasses the state-of-the-art methods.

Video camouflaged object detection aims to identify objects that are visually concealed within the surroundings in a video. Most of the existing methods fall into analyzing the implicit inter-frame motion to capture the camouflaged object. However, due to a lack of exploring the prior explicit motion of the camouflaged object, these works generally encounter difficulty in capturing the complete camouflaged object. To address this issue, we propose to integrate implicit and explicit motion learning into a unified framework, namely Im plicit- Ex plicit Motion Learning network (IMEX), for video camouflaged object detection. Specifically, to promote the identifiability of the camouflaged object, a cross-scale representation fusion was proposed for global inter-frame alignment. By establishing cross-scale temporal-spatial association and aggregating the temporal-spatial attentive representations, it also achieves an elimination of the implicit motion of inter-frame to some extent. Moreover, to further improve the discriminability of boundary regions of the detected object, an explicit motion-induced consistency preserving of camouflaged objects is proposed, in which the prior boundary-aware explicit motion field is leveraged to supervise the consistency of camouflaged objects in consecutive frames. Extensive experiments show that our proposed IMEX achieves substantial performance improvements by a large margin.