Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments in video and audio, offering strong interpretability for security and forensics. While recent State Space Models (SSMs) show promise in precise temporal reasoning, their use in TFL is hindered by ambiguous boundaries, sparse forgeries, and limited long-range modeling. We propose DeformTrace, which enhances SSMs with deformable dynamics and relay mechanisms to address these challenges. Specifically, Deformable Self-SSM (DS-SSM) introduces dynamic receptive fields into SSMs for precise temporal localization. To further enhance its capacity for temporal reasoning and mitigate long-range decay, a Relay Token Mechanism is integrated into DS-SSM. Besides, Deformable Cross-SSM (DC-SSM) partitions the global state space into query-specific subspaces, reducing non-forgery information accumulation and boosting sensitivity to sparse forgeries. These components are integrated into a hybrid architecture that combines the global modeling of Transformers with the efficiency of SSMs. Extensive experiments show that DeformTrace achieves state-of-the-art performance with fewer parameters, faster inference, and stronger robustness.

Despite rapid progress, Video Large Language Models (Video-LLMs) remain unreliable due to hallucinations, which are outputs that contradict either video evidence (faithfulness) or verifiable world knowledge (factuality).Existing benchmarks provide limited coverage of factuality hallucinations and predominantly evaluate models only in clean settings.We introduce INFACT, a diagnostic benchmark comprising 9,800 QA instances with fine-grained taxonomies for faithfulness and factuality, spanning real and synthetic videos.INFACT evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items.Reliability under induced modes is quantified using Resist Rate (RR) and Temporal Sensitivity Score (TSS).Experiments on 14 representative Video-LLMs reveal that higher Base-mode accuracy does not reliably translate to higher reliability in the induced modes, with evidence corruption reducing stability and temporal intervention yielding the largest degradation.Notably, many open-source baselines exhibit nearzero TSS on factuality, indicating pronounced temporal inertia on order-sensitive questions.

The financial industry faces a substantial workload in verifying document images.Existing methods based on visual features struggle to identify fraudulent document images due to the lack of visual clues on the tampering region.This paper proposes CSIAD (Cross-Sample Image Anomaly Detection) by leveraging LLMs to identify logical inconsistencies in similar images.This novel framework accurately detects forged images with slight tampering traces and explains anomaly detection results.Furthermore, we introduce CrossCred, a new benchmark of real-world fraudulent images with fine-grained manual annotations.Experiments demonstrate that CSIAD outperforms state-of-the-art image fraud detection methods by 79.6% (F 1) on CrossCred and deployed industrial solutions by 21.7% (F 1) on business data.The benchmark is available at https://github.com/XMUDM/CSIAD.

Inverse lithography technology (ILT) is an advanced resolution enhancement technique that achieves mask optimization at the pixel level. However, application of ILT is hindered by time-intensive physical simulation. Herein, we propose an efficient ILT algorithm leveraging a deep learning model. A novel loss function is constructed to guide the model training in a self-supervised manner, eliminating the requirement of labelled data that might be nontrivial to acquire. The trained model outputs final mask patterns without further ILT optimization. Sub-resolution assist features (SRAFs) are generated automatically, the complexity of which can be adjusted during the training process to control mask manufacturability. The model was trained and validated on ICCAD-2013 CAD contest dataset. Better pattern fidelity and up to 12,000 times speedup are observed compared to other SOTA models. The trained model also shows good generalization ability to geometrically-different design patterns from another dataset, via a few-shot learning approach.

Temporal forgery in multimedia-where audio or video streams are subtly manipulated-poses critical challenges for content authenticity verification. While video-level detection has advanced, Temporal Forgery Localization (TFL) remains underexplored, often limited by weak audio-visual modeling and reliance on non-learnable post-processing. To address these challenges, we propose RegQAV, a Register-enhanced Query-based Audio-Visual framework for TFL. RegQAV exploits pretrained foundation models to capture fine-grained audio-visual correspondences and learnable registers are introduced to mitigate the model's tendency to overly focus on a limited set of temporal features. A query-based localization strategy enables end-to-end optimization without post-processing. We also introduce a Modality Fusion Adapter (MFA) for effective multi-scale integration of audio-visual data, a Deepfake Queries Generation (DQG) module for efficient query initialization, and a Poisson Count-Based Approach to dynamically predict the number of forgeries. Experiments on LAV-DF and AV-Deepfake1M show that RegQAV achieves state-of-the-art performance with fewer parameters, faster inference, and stronger generalization. This work offers significant potential for real-time deepfake detection and other multimedia verification applications. The code is available at https://github.com/zxd3099/RegQAV.

This article presents the finite-frequency optimization fault detection (FD) strategy for Takagi-Sugeno (T-S) fuzzy systems. Under the imperfect premise matching (IPM) policy, a weighted fuzzy FD observer (WFFDO) with the$L_{\infty }/L_{2}$robustness performance and the finite-frequency$ H_{-}$fault sensitivity performance is first proposed, which signifies the residual signal is robust to the external interference and sensitive to potential faults. Some parameters and slack matrices are introduced to obtain more relaxed conditions of designing the WFFDO with mixed performance. Afterward, a new online membership functions (MFs) iterative learning algorithm with the exponential decay learning rate is proposed for the sake of updating the observer MFs in real-time such that optimal$L_{\infty }/L_{2}$performance can be achieved in this article. In addition, sufficient criterion is established so as to ensure the convergence of the structured mean squared error cost function by means of Lyapunov stability theory. Eventually, two simulation examples are given for illustrating the feasibility and superiority of the developed optimization FD technique.

This article investigates the issues of state estimation and state estimation-based stabilization for Boolean control networks (BCNs). Unlike previous state observers, this article proposes an optimal state estimator by designing a particular input sequence for the first time, where the maximum-minimum method is employed such that the state of BCNs can be uniquely estimated in short time steps. A minimum reconstructible state set (MRSS) is constructed to determine this input sequence. Next, based on the estimated state, a finite-time stabilization scheme is proposed by constructing a switching controller consisting of three stages. A controller is first developed to estimate the state of BCNs in finite-time steps, and a state reachable controller is also provided to make the state of BCNs reachable to a given equilibrium point. Subsequently, a constant controller is further developed to stabilize the state of BCNs to the equilibrium point. Finally, an oxidative stress response model is used to illustrate the effectiveness of the proposed results.

This article studies the problem of adaptive event-triggered-based reduced-order fault detection (FD) filter design for a category of nonlinear systems with complex communication channel under interval type-2 (IT2) fuzzy-approximation technique. For the purpose of further saving communication resources, a new adaptive event-triggered mechanism equipped with more comprehensive system information is proposed, which can also ensure that the data transmission rate is not lower than the minimum limit predesigned in real time. Considering the complexity of communication network environment, a new data transmission mathematical model with fading measurements and network induced delays is constructed. In addition, to decrease the design complexity of FD systems, a reduced-order FD filter subject to the adaptive event-triggered mechanism as well as mismatched membership functions is devised. By means of the Lyapunov theory, the sufficient criteria are derived to ensure stochastic stability with$H_{\infty }$index for the FD systems. Finally, two practical examples are provided for the sake of verifying the effectiveness of the presented FD technique.

This article investigates the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels. The fading phenomenon existing in output channels is modeled as an independent Gaussian distribution with mathematical expectation and variance. An event-triggered condition along both iteration domain and time domain is constructed in order to save the communication resources in the iteration. The considered nonlinear system is converted into an equivalent linearization model and then the event-triggered MFAILC independent of the system model is constructed with the faded outputs. Rigorous analysis and convergence proof are developed to verify the ultimately boundedness of the tracking error by using the Lyapunov function. Finally, the effectiveness of the presented algorithm is demonstrated with a numerical example and a velocity tracking control example of wheeled mobile robots (WMRs).

This paper presents a secure chaotic communication scheme using observer-based synchronization of chaotic systems. A class of chaotic systems satisfying incremental quadratic constraints are considered as the transmitter. The original message is encrypted in the noise-like output of the system. A circle criterion-based observer is designed for the system as the receiver by using the linear matrix inequalities technique. The message is recovered at the receiver end from the estimation error of the observer. To design the observer, a general algorithm to calculate incremental multiplier matrices for nonlinearities in chaotic systems is presented. Moreover, the proposed observer-based chaotic secure communication scheme is feasible for many common chaotic systems with ellipsoidal bounds. The simulation results show the good performance of the proposed communication scheme through an example of image transmission.