Search Results (110)

This paper presents HE-DMDeception, a novel adversarial attack network that integrates human visual deception with deep model deception to enhance the security of 3D object detection. Existing patch-based and camouflage methods can mislead deep learning models but struggle to generate visually imperceptible, high-quality textures. Our framework employs a CycleGAN-based camouflage network to generate highly camouflaged background textures, while a dedicated deception module disrupts non-maximum suppression (NMS) and attention mechanisms through optimized constraints that balance attack efficacy and visual fidelity. To overcome the scarcity of annotated vehicle data, an image segmentation module based on the pre-trained Segment Anything (SAM) model is introduced, leveraging a two-stage training strategy combining semi-supervised self-training and supervised fine-tuning. Experimental results show that the minimum P@0.5 values (50%, 55%, 20%, 25%, 25%) were achieved by HE-DMDeception across You Only Look Once version 8 (YOLOv8), Real-Time Detection Transformer (RT-DETR), Fast Region-based Convolutional Neural Network (Faster-RCNN), Single Shot MultiBox Detector (SSD), and MaskRegion-based Convolutional Neural Network (Mask RCNN) detection models, while maintaining high visual consistency with the original camouflage. These findings demonstrate the robustness and practicality of HE-DMDeception, offering new insights into 3D object detection adversarial attacks. Full article

Synthetic aperture radar (SAR) exhibits all-day and all-weather capabilities, granting it significant application in remote sensing. However, interpreting SAR images requires extensive expertise, making SAR-to-optical remote sensing image translation a crucial research direction. While conditional generative adversarial networks (CGANs) have demonstrated exceptional performance in image translation tasks, their massive number of parameters pose substantial challenges. Therefore, this paper proposes ILF-BDSNet, a compressed network for SAR-to-optical image translation. Specifically, first, standard convolutions in the feature-transformation module of the teacher network are replaced with depthwise separable convolutions to construct the student network, and a dual-resolution collaborative discriminator based on PatchGAN is proposed. Next, knowledge distillation based on intermediate-layer features and channel pruning via weight sharing are designed to train the student network. Then, the bio-inspired dynamic search of channel configuration (BDSCC) algorithm is proposed to efficiently select the optimal subnet. Meanwhile, the pixel-semantic dual-domain alignment loss function is designed. The feature-matching loss within this function establishes an alignment mechanism based on intermediate-layer features from the discriminator. Extensive experiments demonstrate the superiority of ILF-BDSNet, which significantly reduces number of parameters and computational complexity while still generating high-quality optical images, providing an efficient solution for SAR image translation in resource-constrained environments. Full article

Object detection is used to automatically identify and locate specific objects within images or videos for applications like autonomous driving, security surveillance, and medical imaging. Protecting object detection models against adversarial attacks, particularly malicious patches, is crucial to ensure reliable and safe performance in safety-critical applications, where misdetections can lead to severe consequences. Existing defenses against patch attacks are primarily designed for stationary scenes and struggle against adversarial image patches that vary in scale, position, and orientation in dynamic environments.In this paper, we introduce SAR, a patch-agnostic defense scheme based on image preprocessing that does not require additional model training. By integration of the patch-agnostic detection frontend with an additional broken pixel restoration backend, Segment and Recover (SAR) is developed for the large-mask-covered object-hiding attack. Our approach breaks the limitation of the patch scale, shape, and location, accurately localizes the adversarial patch on the frontend, and restores the broken pixel on the backend. Our evaluations of the clean performance demonstrate that SAR is compatible with a variety of pretrained object detectors. Moreover, SAR exhibits notable resilience improvements over state-of-the-art methods evaluated in this paper. Our comprehensive evaluation studies involve diverse patch types, such as localized-noise, printable, visible, and adaptive adversarial patches. Full article

In vision-guided autonomous robots, object detectors play a crucial role in perceiving the environment for path planning and decision-making. However, adaptive adversarial patch attacks undermine the resilience of detector-based systems. Strengthening object detectors against such adaptive attacks enhances the robustness of navigation systems. Existing defenses against patch attacks are primarily designed for stationary scenes and struggle against adaptive patch attacks that vary in scale, position, and orientation in dynamic environments. In this paper, we introduce Ad_YOLO+, an efficient and effective plugin that extends Ad_YOLO to defend against white-box patch-based image attacks. Built on YOLOv5x with an additional patch detection layer, Ad_YOLO+ is trained on a specially crafted adversarial dataset (COCO-Visdrone-2019). Unlike conventional methods that rely on redundant image preprocessing, our approach directly detects adversarial patches and the overlaid objects. Experiments on the adversarial training dataset demonstrate that Ad_YOLO+ improves both provable robustness and clean accuracy. Ad_YOLO+ achieves $85.4\%$ top-1 clean accuracy on the COCO dataset and $74.63\%$ top-1 robust provable accuracy against pixel square patches anywhere on the image for the COCO-VisDrone-2019 dataset. Moreover, under adaptive attacks in AirSim simulations, Ad_YOLO+ reduces the attack success rate, ensuring tracking resilience in both dynamic and static settings. Additionally, it generalizes well to other patch detection weight configurations. Full article

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to 3.5 days. However, applications that require monitoring intervals of less than three days or cloudy data can limit the usage of HLS data. Gaofen-1 (GF-1) Wide Field of View (WFV) data provides the capacity further to enhance the data availability by harmonization with HLS. In this study, GF-1/WFV data is harmonized with HLS by employing deep learning-based conditional Generative Adversarial Networks (cGANs). The harmonized WFV data with HLS provides an average temporal resolution of 1.5 days (ranging from 1.2 to 1.7 days), whereas the temporal resolution of HLS varies from 2.8 to 3.5 days. This enhanced temporal resolution will benefit applications that require frequent monitoring. Various processes are employed in HLS to achieve seamless products from the Operational Land Imager (OLI) and Multispectral Imager (MSI). This study applies 6S atmospheric correction to obtain GF-1/WFV surface reflectance data, employs MFC cloud masking, resamples the data to 30 m, and performs geographical correction using AROP relative to HLS data, to align preprocessing with HLS workflows. Harmonization is achieved without using BRDF normalization and bandpass adjustment like in the HLS workflows; instead, cGAN learns cross-sensor reflectance mapping by utilizing a U-Net generator and a patchGAN discriminator. The harmonized GF-1/WFV data were compared to the reference HLS data using various quality indices, including SSIM, MBE, and RMSD, across 126 cloud-free validation tiles covering various land covers and seasons. Band-wise scatter plots, histograms, and visual image color quality were compared. All these indices, including the Sobel filter, histograms, and visual comparisons, indicated that the proposed method has effectively reduced the spectral discrepancies between the GF-1/WFV and HLS data. Full article

Despite the remarkable success of Deep Neural Networks (DNNs) in Remote Sensing Image (RSI) object detection, they remain vulnerable to adversarial attacks. Numerous adversarial attack methods have been proposed for RSI; however, adding a single large-scale adversarial patch to certain high-value targets, which are typically large in physical scale and irregular in shape, is both costly and inflexible. To address this issue, we propose a strategy of using multiple compact patches. This approach introduces two fundamental challenges: (1) how to optimize patch placement for a synergistic attack effect, and (2) how to retain strong adversarial potency within size-constrained mini-patches. To overcome these challenges, we introduce the Spatially Adaptive and Distillation-Enhanced Mini-Patch Attack (SDMPA) framework, which consists of two key modules: (1) an Adaptive Sensitivity-Aware Positioning (ASAP) module, which resolves the placement challenge by fusing the model’s attention maps from both an explainable and an adversarial perspective to identify optimal patch locations, and (2) a Distillation-based Mini-Patch Generation (DMPG) module, which tackles the potency challenge by leveraging knowledge distillation to transfer adversarial information from large teacher patches to small student patches. Extensive experiments on the RSOD and MAR20 datasets demonstrate that SDMPA significantly outperforms existing patch-based attack methods. For example, against YOLOv5n on the RSOD dataset, SDMPA achieves an Attack Success Rate (ASR) of 88.3% using only three small patches, surpassing other patch attack methods. Full article

An image super-resolution (SR) method based on Generative Adversarial Networks (GANs) has achieved impressive results in terms of visual performance. However, the weights of loss functions in these methods are usually set to fixed values manually, which cannot fully adapt to different datasets and tasks, and may result in a decrease in the perceptual effect of the SR images. To address this issue and further improve visual quality, we propose a perception-driven SupGAN, which improves the generator and loss function of GAN-based image super-resolution models. The generator adopts multi-scale feature extraction and fusion to restore SR images with diverse and fine textures. We design a network-training method based on the proportion of high-frequency information in images (BHFTM), which utilizes the proportion of high-frequency information in images obtained through the Canny operator to set the weights of the loss function. In addition, we employ the four-patch method to better simulate the degradation of complex real-world scenarios. We extensively test our method and compare it with recent SR methods (BSRGAN, Real-ESRGAN, RealSR, SwinIR, LDL, etc.) on different types of datasets (OST300, 2020track1, RealWorld38, BSDS100 etc.) with a scaling factor of ×4. The results show that the NIQE metric improves, and also demonstrate that SupGAN can generate more natural and fine textures while suppressing unpleasant artifacts. Full article

Robust perception of road surface conditions is a critical challenge for the safe deployment of autonomous vehicles and the efficient management of transportation infrastructure. This paper introduces a synthetic data-driven deep learning framework designed to address this challenge. We present a large-scale, procedurally generated 3D synthetic dataset created in Blender, featuring a diverse range of road defects—including cracks, potholes, and puddles—alongside crucial road features like manhole covers and patches. Crucially, our dataset provides dense, pixel-perfect annotations for segmentation masks, depth maps, and camera parameters (intrinsic and extrinsic). Our proposed model leverages these rich annotations in a multi-task learning framework that jointly performs road defect segmentation and depth estimation, enabling a comprehensive geometric and semantic understanding of the road environment. A core contribution is a two-stage domain adaptation strategy to bridge the synthetic-to-real gap. First, we employ a modified CycleGAN with a segmentation-aware loss to translate synthetic images into a realistic domain while preserving defect fidelity. Second, during model training, we utilize a dual-discriminator adversarial approach, applying alignment at both the feature and output levels to minimize domain shift. Benchmarking experiments validate our approach, demonstrating high accuracy and computational efficiency. Our model excels in detecting subtle or occluded defects, attributed to an occlusion-aware loss formulation. The proposed system shows significant promise for real-time deployment in autonomous navigation, automated infrastructure assessment and Advanced Driver-Assistance Systems (ADAS). Full article

A novel multimodal super-resolution framework is introduced, combining GAN-based synthesis, perceptual constraints, and joint low-rank sparsity regularization to noticeably enhance MR-PET image quality. The architecture integrates modality-specific ResNet encoders, a transformer-based attention fusion block, and a multi-scale PatchGAN discriminator. Training is guided by a hybrid loss function incorporating adversarial, pixel-wise, perceptual (VGG19), and structured Hankel constraints. The proposed method outperforms all baselines in PSNR, SSIM, LPIPS, and diagnostic confidence metrics. Clinical PET metrics, such as SUV recovery and lesion detectability, show substantial improvement. A thorough analysis of computational complexity, dataset composition, training reproducibility, and motion compensation is provided. These findings are visually supported by processed scan panels and benchmark tables. This framework advances reproducible and interpretable hybrid neuroimaging with strong clinical and technical validation. Full article

To address the challenges of image inpainting in scenarios with extensive or irregular missing regions—particularly detail oversmoothing, structural ambiguity, and textural incoherence—this paper proposes an Image Structure-Guided (ISG) framework that hierarchically integrates structural priors with semantic-aware texture synthesis. The proposed methodology advances a two-stage restoration paradigm: (1) Structural Prior Extraction, where adaptive edge detection algorithms identify residual contours in corrupted regions, and a transformer-enhanced network reconstructs globally consistent structural maps through contextual feature propagation; (2) Structure-Constrained Texture Synthesis, wherein a multi-scale generator with hybrid dilated convolutions and channel attention mechanisms iteratively refines high-fidelity textures under explicit structural guidance. The framework introduces three innovations: (1) a hierarchical feature fusion architecture that synergizes multi-scale receptive fields with spatial-channel attention to preserve long-range dependencies and local details simultaneously; (2) spectral-normalized Markovian discriminator with gradient-penalty regularization, enabling adversarial training stability while enforcing patch-level structural consistency; and (3) dual-branch loss formulation combining perceptual similarity metrics with edge-aware constraints to align synthesized content with both semantic coherence and geometric fidelity. Our experiments on the two benchmark datasets (Places2 and CelebA) have demonstrated that our framework achieves more unified textures and structures, bringing the restored images closer to their original semantic content. Full article

The precise identification and non-destructive measurement of structural features and defects in semiconductor wafers are essential for ensuring process integrity and sustaining high yield in advanced manufacturing environments. Unlike conventional measurement techniques, scanning acoustic microscopy (SAM) is an advanced method that provides detailed visualizations of both surface and internal wafer structures. However, in practical industrial applications, the scanning time and image quality of SAM significantly impact its overall performance and utility. Prolonged scanning durations can lead to production bottlenecks, while suboptimal image quality can compromise the accuracy of defect detection. To address these challenges, this study proposes LinearTGAN, an improved generative adversarial network (GAN)-based model specifically designed to improve the resolution of linear acoustic wafer images acquired by the breakthrough rotary scanning acoustic microscopy (R-SAM) system. Empirical evaluations demonstrate that the proposed model significantly outperforms conventional GAN-based approaches, achieving a Peak Signal-to-Noise Ratio (PSNR) of 29.479 dB, a Structural Similarity Index Measure (SSIM) of 0.874, a Learned Perceptual Image Patch Similarity (LPIPS) of 0.095, and a Fréchet Inception Distance (FID) of 0.445. To assess the measurement aspect of LinearTGAN, a lightweight defect segmentation module was integrated and tested on annotated wafer datasets. The super-resolved images produced by LinearTGAN significantly enhanced segmentation accuracy and improved the sensitivity of microcrack detection. Furthermore, the deployment of LinearTGAN within the R-SAM system yielded a 92% improvement in scanning performance for 12-inch wafers while simultaneously enhancing image fidelity. The integration of super-resolution techniques into R-SAM significantly advances the precision, robustness, and efficiency of non-destructive measurements, highlighting their potential to have a transformative impact in semiconductor metrology and quality assurance. Full article

The natural features of the face exhibit significant symmetry. In practical applications, faces may be partially occluded due to factors like wearing masks or glasses, or the presence of other objects. Occluded-face restoration has broad application prospects in fields such as augmented reality, virtual reality, healthcare, security, etc. It is also of significant practical importance in enhancing public safety and providing efficient services. This research establishes an improved occluded-face restoration network based on facial feature points and Generative Adversarial Networks. A facial landmark prediction network is constructed based on an improved MobileNetV3-small network. On the foundation of U-Net, dilated convolutions and residual blocks are introduced to form an enhanced generator network. Additionally, an improved discriminator network is built based on Patch-GAN. Compared to the Contextual Attention network, under various occlusions, the improved face restoration network shows a maximum increase in the Peak Signal-to-Noise Ratio of 24.47%, and in the Structural Similarity Index of 24.39%, and a decrease in the Fréchet Inception Distance of 81.1%. Compared to the Edge Connect network, under various occlusions, the improved network shows a maximum increase in the Peak Signal-to-Noise Ratio of 7.89% and in the Structural Similarity Index of 10.34%, and a decrease in the Fréchet Inception Distance of 27.2%. Compared to the LaFIn network, under various occlusions, the improved network shows a maximum increase in the Peak Signal-to-Noise Ratio of 3.4% and in the Structural Similarity Index of 3.31%, and a decrease in the Fréchet Inception Distance of 9.19%. These experiments show that the improved face restoration network yields better restoration results. Full article

In the area of microstrip filter design and optimization, deep learning (DL) algorithms have become much more attractive and powerful in recent years. Here, we propose a method to realize the inverse design of passive microstrip filters, applying generative adversarial networks (GANs). The proposed DL-assisted framework is composed of three components, including a compositional pattern-producing network GAN-based graphic generator, a convolution neural network (CNN)-based electromagnetic (EM) response predictor, and a genetic algorithm optimizer. The filter adopts a square patch resonator structure with an irregular-graphic slot and corner-cuts introduced at diagonal positions. By constructing a hybrid model of pixelated patterns in the filter structures and the corresponding EM response S-parameters, we can obtain customized filter solutions with wideband and dual-band magnitude responses in the 3–8 GHz and 1–6 GHz frequency range, respectively. For each inverse design, it cost 3.6 min for executing 1000 iterations, on average. Numerical simulations and experimental results show that the S-parameters of the generated filters are in excellent agreement with the self-defined targets. Full article

Pansharpening is a remote sensing image fusion technique that combines a high-resolution (HR) panchromatic (PAN) image with a low-resolution (LR) multispectral (MS) image to produce an HR MS image. The primary challenge in pansharpening lies in preserving the spatial details of the PAN image while maintaining the spectral integrity of the MS image. To address this, this article presents a generative adversarial network (GAN)-based approach to pansharpening. The GAN discriminator facilitated matching the generated image’s intensity to the HR PAN image and preserving the spectral characteristics of the LR MS image. The performance in generating images was evaluated using the peak signal-to-noise ratio (PSNR). For the experiment, original LR MS and HR PAN satellite images were partitioned into smaller patches, and the GAN model was validated using an 80:20 training-to-testing data ratio. The results illustrated that the super-resolution images generated by the SRGAN model achieved a PSNR of 31 dB. These results demonstrated the developed model’s ability to reconstruct the geometric, textural, and spectral information from the images. Full article

Mixed Sample Data Augmentation (MSDA) enhances deep learning model generalization by blending a source patch into a target image. Selecting source patches based on image saliency helps to prevent label errors and irrelevant content; however, it relies on computationally expensive saliency detection algorithms. Studies suggest that a convolutional neural network’s receptive field follows a Gaussian distribution, with central pixels being more influential. Leveraging this, we propose GaussianMix, an effective and efficient augmentation strategy that selects source patches using a center-biased Gaussian distribution in order to avoiding additional computational costs. GaussianMix achieves top-1 error rates of 21.26% and 20.09% on ResNet-50 and ResNet-101 for ImageNet classification, respectively, while also improving robustness against adversarial perturbations and enhancing object detection performance. Full article