In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, present a novel block: Half Block (HIN Block), to boost performance image restoration networks. Based on HIN Block, design simple and powerful multi-stage network named HINet, which consists two subnetworks. With help HINet surpasses state-of-the-art (SOTA) various For denoising, exceed it 0.11dB 0.28 dB PSNR SIDD dataset, with only 7.5% 30% its multiplier-accumulator operations (MACs), 6.8×...

Nations, industries, and aspects of everyday life have undergone forgery counterfeiting ever since the emergence commercialization. Securing documents products with anticounterfeit additives shows promise for authentication, allowing one to combat ever-increasing global counterfeiting. One most-used effective encryption strategy is combine optical-security markers on required protection objects; however, state-of-the-art labels still suffer from imitation due their poor complexity easy...

Despite the tremendous success, deep neural networks are exposed to serious IP infringement risks. Given a target model, if attacker knows its full information, it can be easily stolen by fine-tuning. Even only output is accessible, surrogate model trained through student-teacher learning generating many input-output training pairs. Therefore, protection important and necessary. However, still seriously under-researched. In this work, we propose new watermarking framework for protecting...

Deep learning has achieved tremendous success in numerous industrial applications. As training a good model often needs massive high-quality data and computation resources, the learned models have significant business values. However, these valuable deep are exposed to huge risk of infringements. For example, if attacker full information one target including network structure weights, can be easily finetuned on new datasets. Even only access output model, he/she still train another similar...

Traditional watermarking algorithms have been extensively studied. As an important type of schemes, template-based approaches maintain a very high embedding rate. In such scheme, the message is often represented by some dedicatedly designed templates, and then process carried out additive operation with templates host image. To resist potential distortions, these need to contain special statistical features so that they can be successfully recovered at extracting side. But in existing...

Abstract Direct electrical stimulation, the transient ‘lesional’ method probing brain function, has been utilized in identifying language cortex and preserving function during epilepsy neuro-oncological surgeries for about a century. However, comparison of functional maps across languages/continents based on cortical stimulation remains unclear. We conducted retrospective multicentre study including four cohorts direct mapping from centres three continents, where indigenous languages...

Recent research shows deep neural networks are vulnerable to different types of attacks, such as adversarial attack, data poisoning attack and backdoor attack. Among them, is the most cunning one can occur in almost every stage learning pipeline. Therefore, has attracted lots interests from both academia industry. However, existing methods either visible or fragile some effortless pre-processing common transformations. To address these limitations, we propose a robust invisible called...

Classic black-box adversarial attacks can take advantage of transferable examples generated by a similar substitute model to successfully fool the target model. However, these models need be trained models' training data, which is hard acquire due privacy or transmission reasons. Recognizing limited availability real data for queries, recent works proposed train in data-free scenario. their generative networks (GANs) based framework suffers from convergence failure and collapse, resulting...

Accurate classification or prediction of the brain state across individual subject, i.e., healthy, with disorders, is generally a more difficult task than merely finding group differences. The former must be approached highly informative and sensitive biomarkers as well effective pattern classification/feature selection approaches. In this paper, we propose systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on level. Multiple...

The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task great clinical significance. Despite the progress to chart differences between healthy controls and patients at group level, pattern classification brain networks across individuals still less developed. In this paper we identify two novel neuroimaging measures that prove be strongly predictive markers in epileptic...

In Federated Learning (FL), models are as fragile centrally trained against adversarial examples. However, the robustness of federated learning remains largely unexplored. This paper casts light on challenge learning. To facilitate a better understanding vulnerability existing FL methods, we conduct comprehensive evaluations various attacks and training methods. Moreover, reveal negative impacts induced by directly adopting in FL, which seriously hurts test accuracy, especially non-IID...

The intellectual property of deep networks can be easily "stolen" by surrogate model attack. There has been significant progress in protecting the IP classification tasks. However, little attention devoted to protection image processing models. By utilizing consistent invisible spatial watermarks, work [1] first considered watermarking for and demonstrated its efficacy many downstream Its success depends on hypothesis that if a watermark exists all prediction outputs, will learned into...

Benefiting from the development of generative adversarial networks (GAN), facial manipulation has achieved significant progress in both academia and industry recently. It inspires an increasing number entertainment applications but also incurs severe threats to individual privacy even political security meanwhile. To mitigate such risks, many countermeasures have been proposed. However, great majority methods are designed a passive manner, which is detect whether images or videos tampered...

Model inversion (MI) attacks have raised increasing concerns about privacy, which can reconstruct training data from public models. Indeed, MI be formalized as an optimization problem that seeks private in a certain space. Recent leverage generative adversarial network (GAN) image prior to narrow the search space, and successfully even high-dimensional (e.g., face images). However, these do not fully exploit potential capabilities of target model, still leading vague coupled i.e., different...

Artificial Intelligence Generated Content (AIGC) has advanced significantly, particularly with the development of video generation models such as text-to-video (T2V) and image-to-video (I2V) models. However, like other AIGC types, requires robust content control. A common approach is to embed watermarks, but most research focused on images, limited attention given videos. Traditional methods, which watermarks frame-by-frame in a post-processing manner, often degrade quality. In this paper,...

Heterocyclic scaffolds have broad applications in organic synthesis, resulting the production of essential compounds utilized pharmaceuticals, agrochemicals, and dietary products. In this study, we present characterization a discovered succinic semialdehyde dehydrogenase from Klebsiella pneumoniae (KpSSADH) elucidate its crystallographic structure. Further investigation into catalytic performance KpSSADH reveals remarkable efficiency converting various heteroatomcontaining (including N, S,...

The systems and software powered by Large Language Models (LLMs) Multi-Modal LLMs (MLLMs) have played a critical role in numerous scenarios. However, current LLM are vulnerable to prompt-based attacks, with jailbreaking attacks enabling the system generate harmful content, while hijacking manipulate perform attacker-desired tasks, underscoring necessity for detection tools. Unfortunately, existing detecting approaches usually tailored specific resulting poor generalization various across...

Public opinion polls show that political trust tends to be higher in authoritarian regimes compared liberal democracies. Many scholars have argued respondents may provide false answers out of fear about repercussions by the state, thereby skewing survey results a positive direction. Using an unobtrusive measure based on affect transfer, we find adult participants experiments conducted China transfer toward state onto evaluations television advertisements upon mere exposure name central party...