2026· IEEE Signal Processing Letters· Vol 33, pp. 3466-3470· 0 citations· 30 references
Abstract
The increasing realism of image manipulations poses significant challenges for forgery localization. However, existing methods are hindered by the limited adaptability of constrained frequency filters and the dilution of subtle forensic cues in deep networks. To address these challenges, we propose the Laplacian pyramid reweighting with progressive Residual Learning framework (LapRL-Net). First, a Laplacian Residual Adaptive Reweighting (LRAR) module is introduced to adaptively modulate multi-scale frequency residuals, enabling flexible extraction of discriminative frequency artifacts. Second, to mitigate feature dilution, we design a Progressive global-local Residual Fusion Module (PRFM) with multi-level residual fusion, which progressively combines global contextual dependencies with local texture details to preserve critical forensic cues. Furthermore, an Edge-Guided Refinement Module (EGRM) is incorporated to enhance boundary accuracy by enforcing geometric consistency via edge supervision. Extensive experiments on multiple benchmarks demonstrate that the proposed method achieves competitive performance in complex forensic scenarios.
Visible watermarks are widely used for image copyright protection, but their removal remains a challenging restoration task due to the diversity of watermarks in color, scale, transparency, and spatial distribution. Existing methods predominantly rely on CNN-based frameworks, where limited receptive fields constrain spatial modeling, and contrastive learning is applied at intermediate feature levels rather than directly supervising the final reconstructed output. In this paper, we propose CRFormer, a single-stage Transformer network for blind visible watermark removal. CRFormer replaces the CNN backbone with a full Transformer to model watermark regions of arbitrary spatial extent. To compensate for the reduced spatial sensitivity of channel-wise attention, we introduce a deformable convolution feed-forward network that restores spatial perception and integrates watermark mask prediction directly into the backbone. We further apply contrastive learning as an output-level regularization, where multi-scale VGG features of the reconstructed image are pulled toward the watermark-free ground truth and pushed away from the watermarked input, providing direct supervision over perceptual reconstruction quality that intermediate-feature decoupling cannot offer. Extensive experiments on multiple public benchmarks demonstrate that CRFormer consistently outperforms existing state-of-the-art methods by a significant margin.
Yun Hao, Jiaju Wu, Rui Sun et al.· Neural Networks· 0 citations
High-Compression videos suffer from severe distortions, among which degradation in person regions has the greatest impact on viewers’ immersive experience. Existing quality enhancement techniques usually focus on overall image denoising or super-resolution, often overlooking the crucial recovery of fine structures in these essential person regions. To address these challenges, the research introduces a novel framework titled Person Region Restoration Driven by Perceptual Fidelity (PRRDPF), which combines long-range dependency features with perceptual structure loss for enhanced generative restoration. Specifically, first, the research constructs a high-fidelity distorted person-region dataset via a closed-loop degradation pipeline, addressing the lack of paired datasets. Secondly, a Temporal Gated Fusion (TGF) block is designed to use gated convolutions for selectively recovering high-frequency features while capturing local and global dependencies. Finally, a Structural Similarity Index Measure (SSIM)-based dynamic weighted adversarial loss is proposed to prioritize the restoration of visual texture details. Experimental results validate that PRRDPF significantly outperforms the best models in Peak Signal-to-Noise Ratio (PSNR), SSIM, and Learned Perceptual Image Patch Similarity (LPIPS), effectively mitigating artifacts and enhancing clarity in person visuals. This framework presents a promising approach for intelligent video coding integrated with generative artificial intelligence and holds significant potential for practical applications.
Linyun Liu, Li Yu, Jiaxin Zeng et al.· IEEE Signal Processing Lette...· 0 citations
The rapid proliferation of artificial intelligence-generated content necessitates reliable multimodal forensics. Beyond video-level binary classification, precisely localizing sparsely distributed forged segments in long-form videos remains a critical challenge. This task is particularly difficult when manipulations are subtly embedded and cross-modal signals are weak and temporally diffuse. To address these challenges, we propose EVAS, an end-to-end multimodal framework for temporal forgery localization. At its core, a Multi-Stage Audio-Visual Synergy mechanism facilitates progressive cross-modal interaction to learn deep multimodal forensic representations and capture high-order semantic traces of sparse manipulations. Furthermore, we introduce a Boundary-Aware Refinement strategy to achieve steered boundary calibration. By incorporating invalid-frame masking, this strategy suppresses ambiguous regions and sharpens transition predictions. We adopt a decoupled training paradigm with auxiliary heads to disentangle representation learning from inference objectives, enhancing model generalization and stability. Additionally, a lightweight HourglassFFN is incorporated to reduce computational overhead. Extensive experiments demonstrate that EVAS achieves state-of-the-art average localization accuracy and average recall across three benchmark datasets, validating its effectiveness for fine-grained temporal forgery localization.
Shen Shen, Quan Zhang, Dan Jiang et al.· 0 citations
Image dehazing aims to generate the haze-free images from the hazy observation images. While recent deep learning approaches achieve impressive restoration quality, they suffer from excessive computational complexity and model size, hindering practical applications for real-world deployment on resource-constrained edge devices. To address the limitation, lightweight models are proposed to this end but compromise on dehazing performance. To bridge this gap, we propose SLRNet, Super Lightweight Residual Network, a high efficient-yet-effective end-to-end dehazing architecture. SLRNet integrates a novel Adaptive Feature Unit that automatically adjusts channel-wise features through a lightweight gating mechanism, coupled with compact residual blocks to preserve critical structural information. Unlike standard channel attention mechanisms that discard spatial information, our AFU employs an asymmetric split strategy to simultaneously preserve local texture details and capture global haze density. Our design emphasizes minimal parameter count and low latency without sacrificing perceptual quality. Experiments are carried out across standard benchmarks, showing that our proposed SLRNet demonstrates remarkable performance by achieving state-of-the-art efficiency-accuracy trade-offs compared to existing works, while maintaining robust generalization to real-world haze despite the synthetic-to-real domain gap. The codes are released in https://anonymous.4open.science/r/SLRNet.
Guanheng Qu· Poster Volume 0007 The 2026...· 0 citations
