DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba

Moire patterns arise when two similar repetitive patterns interfere, a phenomenon frequently observed during the capture of images or videos on screens. The color, shape, and location of moire patterns may differ across video frames, posing a challenge in learning information from adjacent frames and preserving temporal consistency. Previous video demoireing methods heavily rely on well-designed alignment modules, resulting in substantial computational burdens. Recently, Mamba, an improved version of the State Space Model (SSM), has demonstrated significant potential for modeling long-range dependencies with linear complexity, enabling efficient temporal modeling in video demoireing without requiring a specific alignment module. In this paper, we propose a novel alignment-free Raw video demoireing network with frequency-assisted spatio-temporal Mamba (DemMamba). The Spatial Mamba Block (SMB) and Temporal Mamba Block (TMB) are sequentially arranged to facilitate effective intra- and inter-relationship modeling in Raw videos with moire patterns. Within SMB, an Adaptive Frequency Block (AFB) is introduced to aid demoireing in the frequency domain. For TMB, a Channel Attention Block (CAB) is embedded to further enhance temporal information interactions by exploiting the inter-channel relationships among features. Extensive experiments demonstrate that our proposed DemMamba surpasses state-of-the-art approaches by 1.3 dB and delivers a superior visual experience.

Learning A Low-Level Vision Generalist via Visual Task Prompt

Building a unified model for general low-level vision tasks holds significant research and practical value. Current methods encounter several critical issues. Multi-task restoration approaches can address multiple degradation-to-clean restoration tasks, while their applicability to tasks with different target domains (e.g., image stylization) is limited. Methods like PromptGIP can handle multiple input-target domains but rely on the Masked Autoencoder (MAE) paradigm. Consequently, they are tied to the ViT architecture, resulting in suboptimal image reconstruction quality. In addition, these methods are sensitive to prompt image content and often struggle with low-frequency information processing. In this paper, we propose a Visual task Prompt-based Image Processing (VPIP) framework to overcome these challenges. VPIP employs visual task prompts to manage tasks with different input-target domains and allows flexible selection of backbone network suitable for general tasks. Besides, a new prompt cross-attention is introduced to facilitate interaction between the input and prompt information. Based on the VPIP framework, we train a low-level vision generalist model, namely GenLV, on 30 diverse tasks. Experimental results show that GenLV can successfully address a variety of low-level tasks, significantly outperforming existing methods both quantitatively and qualitatively. Codes are available at https://github.com/chxy95/GenLV.

DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross Domain

This work investigates efficient score-based black-box adversarial attacks with a high Attack Success Rate (ASR) and good generalizability. We design a novel attack method based on a \textit{Hierarchical} \textbf{Di}sentangled \textbf{F}eature space and \textit{cross domain}, called \textbf{DifAttack++}, which differs significantly from the existing ones operating over the entire feature space. Specifically, DifAttack++ firstly disentangles an image's latent feature into an \textit{adversarial feature} (AF) and a \textit{visual feature} (VF) via an autoencoder equipped with our specially designed \textbf{H}ierarchical \textbf{D}ecouple-\textbf{F}usion (HDF) module, where the AF dominates the adversarial capability of an image, while the VF largely determines its visual appearance. We train such autoencoders for the clean and adversarial image domains respectively, meanwhile realizing feature disentanglement, by using pairs of clean images and their Adversarial Examples (AEs) generated from available surrogate models via white-box attack methods. Eventually, in the black-box attack stage, DifAttack++ iteratively optimizes the AF according to the query feedback from the victim model until a successful AE is generated, while keeping the VF unaltered. Extensive experimental results demonstrate that our method achieves superior ASR and query efficiency than SOTA methods, meanwhile exhibiting much better visual quality of AEs. The code is available at https://github.com/csjunjun/DifAttack.git.

Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer

Hyperspectral images (HSIs) often suffer from noise arising from both intra-imaging mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs, such as global spectral correlation (GSC) and non-local spatial self-similarity (NSS), is crucial for effective denoising. Existing methods tend to independently utilize each of these knowledge components with multiple blocks, overlooking the inherent 3D nature of HSIs where domain knowledge is strongly interlinked, resulting in suboptimal performance. To address this challenge, this paper introduces a spatial-spectral recurrent transformer U-Net (SSRT-UNet) for HSI denoising. The proposed SSRT-UNet integrates NSS and GSC properties within a single SSRT block. This block consists of a spatial branch and a spectral branch. The spectral branch employs a combination of transformer and recurrent neural network to perform recurrent computations across bands, allowing for GSC exploitation beyond a fixed number of bands. Concurrently, the spatial branch encodes NSS for each band by sharing keys and values with the spectral branch under the guidance of GSC. This interaction between the two branches enables the joint utilization of NSS and GSC, avoiding their independent treatment. Experimental results demonstrate that our method outperforms several alternative approaches. The source code will be available at https://github.com/lronkitty/SSRT.

Text-Driven Traffic Anomaly Detection with Temporal High-Frequency Modeling in Driving Videos

Traffic anomaly detection (TAD) in driving videos is critical for ensuring the safety of autonomous driving and advanced driver assistance systems. Previous single-stage TAD methods primarily rely on frame prediction, making them vulnerable to interference from dynamic backgrounds induced by the rapid movement of the dashboard camera. While two-stage TAD methods appear to be a natural solution to mitigate such interference by pre-extracting background-independent features (such as bounding boxes and optical flow) using perceptual algorithms, they are susceptible to the performance of first-stage perceptual algorithms and may result in error propagation. In this paper, we introduce TTHF, a novel single-stage method aligning video clips with text prompts, offering a new perspective on traffic anomaly detection. Unlike previous approaches, the supervised signal of our method is derived from languages rather than orthogonal one-hot vectors, providing a more comprehensive representation. Further, concerning visual representation, we propose to model the high frequency of driving videos in the temporal domain. This modeling captures the dynamic changes of driving scenes, enhances the perception of driving behavior, and significantly improves the detection of traffic anomalies. In addition, to better perceive various types of traffic anomalies, we carefully design an attentive anomaly focusing mechanism that visually and linguistically guides the model to adaptively focus on the visual context of interest, thereby facilitating the detection of traffic anomalies. It is shown that our proposed TTHF achieves promising performance, outperforming state-of-the-art competitors by +5.4% AUC on the DoTA dataset and achieving high generalization on the DADA dataset.

Progressive Poisoned Data Isolation for Training-time Backdoor Defense

Deep Neural Networks (DNN) are susceptible to backdoor attacks where malicious attackers manipulate the model's predictions via data poisoning. It is hence imperative to develop a strategy for training a clean model using a potentially poisoned dataset. Previous training-time defense mechanisms typically employ an one-time isolation process, often leading to suboptimal isolation outcomes. In this study, we present a novel and efficacious defense method, termed Progressive Isolation of Poisoned Data (PIPD), that progressively isolates poisoned data to enhance the isolation accuracy and mitigate the risk of benign samples being misclassified as poisoned ones. Once the poisoned portion of the dataset has been identified, we introduce a selective training process to train a clean model. Through the implementation of these techniques, we ensure that the trained model manifests a significantly diminished attack success rate against the poisoned data. Extensive experiments on multiple benchmark datasets and DNN models, assessed against nine state-of-the-art backdoor attacks, demonstrate the superior performance of our PIPD method for backdoor defense. For instance, our PIPD achieves an average True Positive Rate (TPR) of 99.95% and an average False Positive Rate (FPR) of 0.06% for diverse attacks over CIFAR-10 dataset, markedly surpassing the performance of state-of-the-art methods.

Image Demoireing in RAW and sRGB Domains

Moir\'e patterns frequently appear when capturing screens with smartphones or cameras, potentially compromising image quality. Previous studies suggest that moir\'e pattern elimination in the RAW domain offers greater efficiency compared to demoir\'eing in the sRGB domain. Nevertheless, relying solely on raw data for image demoir\'eing is insufficient in mitigating color cast due to the absence of essential information required for color correction by the Image Signal Processor (ISP). In this paper, we propose perform Image Demoir\'eing concurrently utilizing both RAW and sRGB data (RRID), which is readily accessible in both smartphones and digital cameras. We develop Skip-Connection-based Demoir\'eing Module (SCDM) with specific modules embeded in skip-connections for the efficient and effective demoir\'eing of RAW and sRGB features, respectively. Subsequently, we propose RGB Guided Image Signal Processor (RGISP) to incorporate color information from coarsely demoir\'ed sRGB features during the ISP stage, assisting the process of color recovery. Extensive experiments demonstrate that our RRID outperforms state-of-the-art approaches by 0.62dB in PSNR and 0.003 in SSIM, exhibiting superior performance both in moir\'e pattern removal and color cast correction.

SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models

Current instruction-based editing methods, such as InstructPix2Pix, often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text encoder in diffusion models. To rectify this, this paper introduces SmartEdit, a novel approach to instruction-based image editing that leverages Multimodal Large Language Models (MLLMs) to enhance their understanding and reasoning capabilities. However, direct integration of these elements still faces challenges in situations requiring complex reasoning. To mitigate this, we propose a Bidirectional Interaction Module that enables comprehensive bidirectional information interactions between the input image and the MLLM output. During training, we initially incorporate perception data to boost the perception and understanding capabilities of diffusion models. Subsequently, we demonstrate that a small amount of complex instruction editing data can effectively stimulate SmartEdit's editing capabilities for more complex instructions. We further construct a new evaluation dataset, Reason-Edit, specifically tailored for complex instruction-based image editing. Both quantitative and qualitative results on this evaluation dataset indicate that our SmartEdit surpasses previous methods, paving the way for the practical application of complex instruction-based image editing.

Regroup Median Loss for Combating Label Noise

The deep model training procedure requires large-scale datasets of annotated data. Due to the difficulty of annotating a large number of samples, label noise caused by incorrect annotations is inevitable, resulting in low model performance and poor model generalization. To combat label noise, current methods usually select clean samples based on the small-loss criterion and use these samples for training. Due to some noisy samples similar to clean ones, these small-loss criterion-based methods are still affected by label noise. To address this issue, in this work, we propose Regroup Median Loss (RML) to reduce the probability of selecting noisy samples and correct losses of noisy samples. RML randomly selects samples with the same label as the training samples based on a new loss processing method. Then, we combine the stable mean loss and the robust median loss through a proposed regrouping strategy to obtain robust loss estimation for noisy samples. To further improve the model performance against label noise, we propose a new sample selection strategy and build a semi-supervised method based on RML. Compared to state-of-the-art methods, for both the traditionally trained and semi-supervised models, RML achieves a significant improvement on synthetic and complex real-world datasets. The source code of the paper has been released.

Recoverable Privacy-Preserving Image Classification through Noise-like Adversarial Examples

With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image related services such as classification has become crucial. In this study, we propose a novel privacypreserving image classification scheme that enables the direct application of classifiers trained in the plaintext domain to classify encrypted images, without the need of retraining a dedicated classifier. Moreover, encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key. Specifically, our proposed scheme involves utilizing a feature extractor and an encoder to mask the plaintext image through a newly designed Noise-like Adversarial Example (NAE). Such an NAE not only introduces a noise-like visual appearance to the encrypted image but also compels the target classifier to predict the ciphertext as the same label as the original plaintext image. At the decoding phase, we adopt a Symmetric Residual Learning (SRL) framework for restoring the plaintext image with minimal degradation. Extensive experiments demonstrate that 1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; 2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; 3) our system exhibits satisfactory generalization capability on the encryption, decryption and classification tasks across datasets that are different from the training one; and 4) a high-level of security is achieved against three potential threat models. The code is available at https://github.com/csjunjun/RIC.git.