This article [1] has been retracted at the request of the authors. After publication, the authors conducted a further verification of the experimental code and results. Regrettably, an error was discovered in the implementation of the adaptive encoding template optimization algorithm (Section~3.6), as a consequence of which the experimental results reported in the manuscript cannot be reproduced. As the reported results cannot be reproduced, the validity of the paper's main conclusions cannot be substantiated. The authors determined that retraction is the most responsible course of action in order to prevent misleading future research. All authors were contacted regarding this retraction. Di... More >

The use of deep neural networks in modern surveillance systems enables real-time object detection, facial recognition, and anomaly detection, but they remain vulnerable to adversarial attacks, creating critical security risks. This survey reviews detection methods tailored for real-time surveillance, categorizing domain-specific attacks including gradient-based methods (FGSM, PGD, C&W), physical patches, and temporal attacks on video data. We evaluate detection approaches across six categories: feature-based (LID, frequency analysis), reconstruction-based (autoencoders, GANs), auxiliary model-based, uncertainty-based (Bayesian Networks, MIAD), steganalysis-based, and attention-based (ViTGuar... More >

Hyperspectral image classification is a central task in remote sensing information extraction. Conventional approaches follow a reconstruct-then-classify paradigm, which entails large data volumes, high computational cost, and poor real-time performance. This paper presents an adaptive hyperspectral direct classification method based on computational spectral imaging. A Digital Micromirror Device (DMD) is used to spectrally encode and modulate the incident light, enabling direct output of two-dimensional spatial classification results without reconstructing the three-dimensional spectral data cube. First, a classification-oriented encoding template is designed via Fisher discriminant analys... More >

Digital image manipulation has become increasingly prevalent with the widespread availability of editing tools, raising concerns regarding image authenticity in critical applications. This study presents a passive image forgery detection framework based on multiscale Weber Local Descriptor features extracted from chrominance components and classified using a Support Vector Machine. The proposed method operates without embedded authentication information and focuses on detecting both copy-move and splicing forgeries through texture-based analysis. Experiments were conducted on two benchmark datasets, CASIA v2.0 and MICC F2000, using ten-fold cross-validation. On the CASIA v2.0 dataset, the fr... More >

While deep learning architectures have driven substantial improvements in salient object detection (SOD), effectively handling objects of unpredictable scales and ambiguous categories remains a complex challenge. These issues are fundamentally tied to how networks process multi-level and multi-scale feature representations. To address this, a novel framework is presented that utilizes aggregate interaction modules to fuse spatial features from neighboring network tiers. By employing minimal up-sampling and down-sampling rates, this mechanism significantly minimizes the introduction of noise. Furthermore, self-interaction modules are embedded within each decoder unit to generate highly refine... More >