With the rapid evolution of deepfake technologies and the wide dissemination of digital media, personal privacy is facing increasingly serious security threats. Deepfake proactive forensics, which involves embedding imperceptible watermarks to enable reliable source tracking, serves as a crucial defense against these threats. Although existing methods show strong forensic ability, they rely on an idealized assumption of single watermark embedding, which proves impractical in real-world scenarios. In this paper, we formally define and demonstrate the existence of Multi-Embedding Attacks (MEA) for the first time. When a previously protected image undergoes additional rounds of watermark embedding, the original forensic watermark can be destroyed or removed, rendering the entire proactive forensic mechanism ineffective. To address this vulnerability, we propose a general training paradigm named Adversarial Interference Simulation (AIS). Rather than modifying the network architecture, AIS explicitly simulates MEA scenarios during fine-tuning and introduces a resilience-driven loss function to enforce the learning of sparse and stable watermark representations. Our method enables the model to maintain the ability to extract the original watermark correctly even after a second embedding. Extensive experiments demonstrate that our plug-and-play AIS training paradigm significantly enhances the robustness of various existing methods against MEA.

Deep learning-based image manipulation localization (IML) methods have achieved remarkable performance in recent years, but typically rely on large-scale pixel-level annotated datasets. To address the challenge of acquiring high-quality annotations, some recent weakly supervised methods utilize image-level labels to segment manipulated regions. However, the performance is still limited due to insufficient supervision signals. In this study, we explore a form of weak supervision that improves the annotation efficiency and detection performance, namely scribble annotation supervision. We re-annotated mainstream IML datasets with scribble labels and propose the first scribble-based IML (Sc-IML) dataset. Additionally, we propose the first scribble-based weakly supervised IML framework. Specifically, we employ self-supervised training with a structural consistency loss to encourage the model to produce consistent predictions under multi-scale and augmented inputs. In addition, we propose a prior-aware feature modulation module (PFMM) that adaptively integrates prior information from both manipulated and authentic regions for dynamic feature adjustment, further enhancing feature discriminability and prediction consistency in complex scenes. We also propose a gated adaptive fusion module (GAFM) that utilizes gating mechanisms to regulate information flow during feature fusion, guiding the model toward emphasizing potential tampered regions. Finally, we propose a confidence-aware entropy minimization loss. This loss dynamically regularizes predictions in weakly annotated or unlabeled regions based on model uncertainty, effectively suppressing unreliable predictions. Experimental results show that our method outperforms existing fully supervised approaches in terms of average performance both in-distribution and out-of-distribution.

Deepfake technology has great potential in the field of media and entertainment, but it also brings serious risks, including privacy disclosure and identity fraud. To counter these threats, proactive forensic methods have become a research hotspot by embedding invisible watermark signals to build active protection schemes. However, existing methods are vulnerable to watermark destruction under malicious distortions, which leads to insufficient robustness. Moreover, embedding strong signals may degrade image quality, making it challenging to balance robustness and imperceptibility. Although watermarked images look natural, their underlying structures are often different from the original images, which is ignored by traditional watermarking methods. To address these issues, this paper proposes a proactive watermarking framework called WaveGuard, which explores frequency domain embedding and graph-based structural consistency optimization. In this framework, the watermark is embedded into the high-frequency sub-bands by dual-tree complex wavelet transform (DT-CWT) to enhance the robustness against distortions and deepfake forgeries. By leveraging joint sub-band correlations and selected sub-band combinations, the framework enables robust source tracing and semi-robust deepfake detection. To enhance imperceptibility, we propose a Structural Consistency Graph Neural Network (SC-GNN) that constructs graph representations of the original and watermarked images to ensure structural consistency and reduce perceptual artifacts. Experimental results show that the proposed method performs exceptionally well in face swap and face replay tasks. The code has been published at https://github.com/vpsg-research/WaveGuard.

Obtaining global ocean surface wind speed data with high temporal resolution and spatial coverage is a challenging task. Due to the lack of widely applicable direct measurement methods and algorithms, current research and data products can only achieve good performance in a small spatial range or at low temporal resolution. In this article, a generative adversarial network (GAN) with a transformer structure called Wind Speed Prediction transformer-GAN (WSPTGAN) is proposed to generate wind speed data with good spatial coverage and high temporal resolution for areas. The WSPTGAN is trained with the proposed image-like wind speed data combined partial missing dataset (CPMD), which is combined with the fifth generation of the European Center for Medium-Range Weather Forecast (ECMWF) reanalysis data and Advanced Scatterometer (ASCAT) data from Meteorological Operational satellites. Thanks to the defective data learning mechanism (DDLM), sequential-wise multihead self-attention mechanism (SMSM), and sequence feature adaptive verification mechanism (SFAVM) in the proposed algorithm, the obtained model has good wind speed prediction accuracy with root mean square error (RMSE) of 0.8984 m/s and can achieve multistep 10-min wind speed data generation within the global ocean. After comparison with five state-of-the-art prediction models, it is confirmed that the algorithm in this article is able to make better use of the defective data for learning and prediction of wind field trends in global ocean regions.

The deployment of global navigation satellite system reflectometry (GNSS-R) emerges as a compelling approach for the extraction of ocean surface wind field, primarily due to its exceptional cost-effectiveness, all-weather robustness, and excellent spatiotemporal coverage. Despite these advantages, the insufficient use of various data and the lack of ability to perform multiindicator retrieval limit the performance of existing methods in practical ocean wind field retrieval. To overcome these limitations, this article introduces a novel self-attention-guided ocean surface wind field multiindicator retrieval algorithm based on multimodal observation data augmentation and fusion. Initially, data generation modules are employed to complement high-quality observation data that are not fully provided by GNSS-R system. Subsequently, the multiscale data fusion encoder (MDFE) is implemented to extract and fuse multiple data features of different scales to enhance the utilization ability of data and improve the accuracy of wind field retrieval. Finally, the self-attention multiindicator predictor (SAMIP) is put to use for optimizing the feature attention strategy, achieving accurate retrieval of ocean surface wind speed and direction simultaneously. The proposed method provides a novel solution for the comprehensive utilization of various data products in the GNSS-R system, simultaneously achieves synchronous retrieval of multiple wind field indicators, which are wind speed and direction. In the context of recent advancements in algorithmic development, the proposed algorithm exhibits a notable enhancement in the precision of wind speed and direction retrieval. Benchmarking against ERA5 wind field data, the proposed algorithm achieves a root-mean-square error (RMSE) of 1.23 m/s in wind speed retrieval, demonstrating at least a 9% accuracy improvement compared to five state-of-the-art algorithms from recent years. Furthermore, the RMSE in wind direction retrieval stands at 20.7°, surpassing comparison algorithms by achieving a reduction of over 8% in error. Collectively, these metrics robustly validate the efficacy of the proposed algorithm.

The fundamental goal of hyperspectral anomaly detection (HAD) is the identification of pixels manifesting substantial deviations in spectral attributes when compared to their neighboring pixels. Nevertheless, the intrinsic attributes of hyperspectral images (HSI), characterized by their high-dimensional essence and the interdependencies among spectral bands, frequently exert an influence on the efficacy of anomaly detection( AD). Furthermore, current detection algorithms often fall short in harnessing the inherent information encapsulated within HSI, thereby constraining the network’s expressive potential. In response to these challenges, we introduce a multiview model tailored that amalgamates both global and local features for HAD. Specifically, the proposed method employs an unsupervised learning-based multiview network to simultaneously conduct feature analysis on both global and local attributes within HSI. The model incorporates a dual-component structure, featuring a global module utilizing axial attention for comprehensive global attribute analysis, and a local module employing a convolutional neural network with residual connections to capture fine-grained local features. Subsequently, the global-local multiview anomaly coupling mechanism is applied to consolidate the strengths of distinct perspectives, resulting in the ultimate AD outcomes. Experimental evaluations are performed on seven public HSI datasets, demonstrating superior performance of the proposed method in comparison to other state-of-the-art approaches.

Hyperspectral anomaly detection (HAD) is of great value in both practical and theoretical terms. However, due to the lack of available semantic labels, previous works mainly relied on unsupervised or semi-supervised methods to construct learning models, which inevitably lacked semantic guidance and led to limited anomaly detection (AD) effectiveness. Besides, few previous methods jointly mine spectral and spatial global dependencies, which limits their effectiveness in practical scenarios. To address the above problems, we design a novel self-supervised HAD method, named the Self-Supervised Hyperspectral Anomaly Detection method based on the Finite Spatial-wise Attention. The core of proposed method is the designed Self-Supervised Hyperspectral Anomaly Detection transFormer (SSHADFormer). It explores the specific spectral attributes of hyperspectral images (HSIs) to reconstruct background HSI from a given RGB image, which solves the difficulty of acquiring semantic information and enhances the agility of AD models. In addition, we propose a Finite Spatial-wise Attention mechanism. The mechanism mines the cluster structure of the background spectrum in a data-driven manner, enhancing the discriminative ability between background and anomalous targets while avoiding anomalous targets interference during training. Extensive experiments on six public datasets demonstrate the effectiveness and agility of the proposed method.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) dynamically monitors water depth in shallow waters around islands and reefs. Noise removal is a prerequisite for accurate reconstruction of seafloor topography based on ICESat-2 data products. To this end, we propose a density and distance-based method (DDBM) to extract seafloor signal photons from ICESat-2 data. The DDBM first separates the photons into three parts: above water, water surface, and water column. The water-column photons consist of seafloor signal photons and noise photons. The DDBM adopts a two-step denoising strategy to remove noise in water-column photons to obtain pure and complete signal photons. In the first step, an orientation-variable adaptive ellipse filter is developed, which can adaptively adjust the parameters according to the water depth to remove low-density noise photons. In the second step, a novel distance-based filter (DBF) is designed for stubborn high-density noise clusters. These noise clusters are far from the signal photons, and the DBF removes them by a distance threshold derived from the Otsu threshold method. We select three high-density ICESat-2 datasets to validate the DDBM. Compared with the reference data, the comprehensive evaluation indexes$F$of the DDBM in all datasets are above 0.99, and the highest is 0.998, showing superior performance.

Hyperspectral anomaly detection (HAD) aims to identify anomalous pixels in the image with significant spectral differences from their surrounding background pixels, and has important military and civilian applications. However, challenges such as high spectral similarity between pixels, data redundancy, and lack of prior information pose significant difficulties in HAD. To address these issues, we propose the Selective Siamese Graph Guided Hyperspectral Anomaly Detection method. In the initial phase of this study, we present a spatial-spectral feature dynamic composition module. Guided by the principles of three-way clustering theories, this module excels in achieving heightened precision in feature embedding and graph construction. Subsequently, we introduce the Characteristic Expression Differentiation mechanism, designed to enhance the separation of hidden layer features for heterogeneous data in high-dimensional space by incorporating Siamese network-derived similarity discrimination principles. Lastly, we develop a graph-guided Siamese selective reconstruction module that places a strong emphasis on the differentiation between background and anomaly features. It utilizes background graph data to construct a pure background and concurrently establishes connections across spectral bands. This approach significantly enhances data processing efficiency while reducing computational resource consumption through the elimination of redundancy. Extensive experiments on seven public datasets demonstrate the effectiveness of the method.