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A high‐fidelity colour transfer should align the colour distributions between images and meanwhile avoid the damage to the original structure. However, the traditional methods often fail to yield high‐fidelity transfer results due to some existing tone and structure artefacts. In this study, the authors propose a new framework to effectively integrate the tone and structure refinements of colour transfer. They develop the ideas of image decomposition and gradient guidance to perform tone reconstruction while protecting original structure. Its overall flow includes the five key steps: tone clustering, structure extraction, structure optimisation, gradient‐guided tone reconstruction, and structure restoration. Moreover, they propose an evaluation metric to measure the differences of tone and structure between images. They demonstrate the performance of the proposed method through a number of experiments in visual comparison and objective evaluation. [ABSTRACT FROM AUTHOR]

Automatic decomposition of intrinsic images, especially for complex real-world images, is a challenging under-constrained problem. Thus, we propose a new algorithm that generates and combines multi-scale properties of chromaticity differences and intensity contrast. The key observation is that the estimation of image reflectance, which is neither a pixel-based nor a region-based property, can be improved by using multi-scale measurements of image content. The new algorithm iteratively coarsens a graph reflecting the reflectance similarity between neighbouring pixels. Then multi-scale reflectance properties are aggregated so that the graph reflects the reflectance property at different scales. This is followed by a L0 sparse regularization on the whole reflectance image, which enforces the variation in reflectance images to be high-frequency and sparse. We formulate this problem through energy minimization which can be solved efficiently within a few iterations. The effectiveness of the new algorithm is tested with the Massachusetts Institute of Technology (MIT) dataset, the Intrinsic Images in the Wild (IIW) dataset, and various natural images. [ABSTRACT FROM AUTHOR]

The objective of intrinsic images estimation is to decompose an input image into its intrinsic shading and reflectance components. This is a well-known under-constrained problem that has long been an open challenge. This paper proposes a novel approach for automatic intrinsic images decomposition that uses a new reflectance sparsity prior. On the basis of the observation that the reflectance of natural objects is commonly piecewise constant, we formalize this constraint on the entire reflectance image using the $$L_0$$ sparse loss function that enforces the variation in reflectance images to be of high-frequency and sparse. This new sparsity constraint significantly improves the quality of Retinex intrinsic images estimation. It also functions effectively by combining a class of global sparsity priors on reflectance. Experimental results on MIT benchmark dataset as well as various real-world images and synthetic images demonstrate the effectiveness and versatility of our approach. [ABSTRACT FROM AUTHOR]

Real-world face recognition systems are vulnerable to diverse face attacks, ranging from digitally manipulated artifacts to physically crafted spoofing attacks. Existing works primarily focus on using an image classification network to address one type of attack but disregarding another. However, face recognition systems in real-world scenarios always encounter diverse simultaneous attacks, rendering the aforementioned single-attack detecting solution ineffective. Besides, excessive reliance on a classifier might easily fail when encountering face attacks with unknown patterns, as the category-level difference learned by classification backbones cannot generalize well to new attacks. Considering that real data are captured from actual individuals, while attack samples are generated by various distinct techniques, our focus is on extracting compact representations of real faces. This approach allows us to identify the fundamental differences between genuine and attack images, enabling us to address both manipulated artifacts and spoofing attacks simultaneously. Concretely, we propose a dual space reconstruction learning framework that models the commonalities of genuine faces in both spatial and frequency domains. With the learned characteristics of real faces, the model is more likely to segregate diverse attack samples as outliers from genuine images. Besides, we introduce a dynamic filtering module that filters out the redundant information retained by the reconstruction and enhances the critical divergence between the real and the attack to achieve better classification features. Since the training samples only cover limited style variations, which hampers the generalization to unseen domains, we further design a consistency regularized training strategy that mimics distribution shifts during training and imposes specific constraints to encourage style-irrelevant features. Moreover, in view of the lack of accessible benchmarks for unified evaluation of the detection competence against both face forgery and spoofing attacks, we set up a new challenging benchmark, named UniAttack, to foster the exploration of effective solutions to face attack detection. Both qualitative and quantitative results from existing and proposed benchmarks unequivocally demonstrate the superiority of our methods over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Although great progress has been made in new electrolytes for lithium metal batteries (LMBs), the intrinsic relationship between electrolyte composition and cell performance remains unclear due to the lack of valid quantization method. Here, we proposed the concept of negative center of electrostatic potential (NCESP) and Mayer bond order (MBO) to describe solvent capability, which highly relate to solvation structure and oxidation potential, respectively. Based on established principles, the selected electrolyte with 1.7 M LiFSI in methoxytrimethylsilane (MOTMS)/ (trifluoromethyl)trimethylsilane (TFMTMS) shows unique hyperconjugation nature to stabilize both Li anode and high‐voltage cathode. The 4.6 V 30 μm Li||4.5 mAh cm−2 lithium cobalt oxide (LCO) (low N/P ratio of 1.3) cell with our electrolyte shows stable cycling with 91 % capacity retention over 200 cycles. The bottom‐up design concept of electrolyte opens up a general strategy for advancing high‐voltage LMBs. [ABSTRACT FROM AUTHOR]

Although great progress has been made in new electrolytes for lithium metal batteries (LMBs), the intrinsic relationship between electrolyte composition and cell performance remains unclear due to the lack of valid quantization method. Here, we proposed the concept of negative center of electrostatic potential (NCESP) and Mayer bond order (MBO) to describe solvent capability, which highly relate to solvation structure and oxidation potential, respectively. Based on established principles, the selected electrolyte with 1.7 M LiFSI in methoxytrimethylsilane (MOTMS)/ (trifluoromethyl)trimethylsilane (TFMTMS) shows unique hyperconjugation nature to stabilize both Li anode and high‐voltage cathode. The 4.6 V 30 μm Li||4.5 mAh cm−2 lithium cobalt oxide (LCO) (low N/P ratio of 1.3) cell with our electrolyte shows stable cycling with 91 % capacity retention over 200 cycles. The bottom‐up design concept of electrolyte opens up a general strategy for advancing high‐voltage LMBs. [ABSTRACT FROM AUTHOR]

The document is a correction to an article titled "Continual Face Forgery Detection via Historical Distribution Preserving" published in the International Journal of Computer Vision. The correction addresses an error in Tables, specifically Table 6 being duplicated and presented as Table 5 in the original article. The corrected version of the article has been updated, and the publisher remains neutral regarding jurisdictional claims. The authors are affiliated with Xiamen University and Tencent in China. [Extracted from the article]

Co-salient object detection (CoSOD) aims to detect common salient objects sharing the same attributes in an image group. The key issue of CoSOD is how to model the inter-saliency relations within an image group. The major limitation of previous methods is that they pre-define the group-to-one relations within an image group. In this paper, we propose a new concept of structural inter-saliency relations and solve the CoSOD with deep reinforcement learning framework. Firstly, we design a semantic relation graph (SRG) to model the structural inter-saliency relations. Then the feature selecting agent (FS-agent) aims to select the informative features, which can help the SRG effectively model structural inter-saliency relations. Finally, relation updating agent (RU-agent) progressively updates the SRG to focus on the co-salient relations like human decision-making process. Extensive experiments on co-saliency datasets show that because of well modeling inter-saliency relations in image group, our proposed method achieves superior performance compared to the state-of-the-art methods. We hope that this paper can motivate future research for visual co-analysis tasks. [ABSTRACT FROM AUTHOR]

Face forgery techniques have advanced rapidly and pose serious security threats. Existing face forgery detection methods try to learn generalizable features, but they still fall short of practical application. Additionally, finetuning these methods on historical training data is resource-intensive in terms of time and storage. In this paper, we focus on a novel and challenging problem: Continual Face Forgery Detection (CFFD), which aims to efficiently learn from new forgery attacks without forgetting previous ones. Specifically, we propose a Historical Distribution Preserving (HDP) framework that reserves and preserves the distributions of historical faces. To achieve this, we use universal adversarial perturbation (UAP) to simulate historical forgery distribution, and knowledge distillation to maintain the distribution variation of real faces across different models. We also construct a new benchmark for CFFD with three evaluation protocols. Our extensive experiments on the benchmarks show that our method outperforms the state-of-the-art competitors. Our code is available at https://github.com/skJack/HDP. [ABSTRACT FROM AUTHOR]

The challenge in sourcing attribution for forgery faces has gained widespread attention due to the rapid development of generative techniques. While many recent works have taken essential steps on GAN-generated faces, more threatening attacks related to identity swapping or diffusion models are still overlooked. And the forgery traces hidden in unknown attacks from the open-world unlabeled faces remain under-explored. To push the related frontier research, we introduce a novel task named Open-World DeepFake Attribution, and the corresponding benchmark OW-DFA++, which aims to evaluate attribution performance against various types of fake faces in open-world scenarios. Meanwhile, we propose a Multi-Perspective Sensory Learning (MPSL) framework that aims to address the challenge of OW-DFA++. Since different forged faces have different tampering regions and frequency artifacts, we introduce the Multi-Perception Voting (MPV) module, which aligns inter-sample features based on global, multi-scale local, and frequency relations. The MPV module effectively filters and groups together samples belonging to the same attack type. Pseudo-labeling is another common and effective strategy in semi-supervised learning tasks, and we propose the Confidence-Adaptive Pseudo-labeling (CAP) module, using soft pseudo-labeling to enhance the class compactness and mitigate pseudo-noise induced by similar novel attack methods. The CAP module imposes strong constraints and adaptively filters samples with high uncertainty to improve the accuracy of the pseudo-labeling. In addition, we extend the MPSL framework with a multi-stage paradigm that leverages pre-train technique and iterative learning to further enhance traceability performance. Extensive experiments and visualizations verify the superiority of our proposed method on the OW-DFA++ and demonstrate the interpretability of the deepfake attribution task and its impact on improving the security of the deepfake detection area. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, the core problem is to well model inter-image relations between an image group. Some methods design sophisticated modules, such as recurrent neural network (RNN), to address this problem. However, order-sensitive problem is the major drawback of RNN, which heavily affects the stability of proposed CoSOD (CoSEG) model. In this paper, inspired by RNN-based model, we first propose a multi-path stable recurrent unit (MSRU), containing dummy orders mechanisms (DOM) and recurrent unit (RU). Our proposed MSRU not only helps CoSOD (CoSEG) model captures robust inter-image relations, but also reduces order-sensitivity, resulting in a more stable inference and training process. Moreover, we design a cross-order contrastive loss (COCL) that can further address order-sensitive problem by pulling close the feature embedding generated from different input orders. We validate our model on five widely used CoSOD datasets (CoCA, CoSOD3k, Cosal2015, iCoseg and MSRC), and three widely used datasets (Internet, iCoseg and PASCAL-VOC) for object co-segmentation, the performance demonstrates the superiority of the proposed approach as compared to the state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]