FogFool creates fog-based adversarial perturbations using Perlin noise optimization to achieve high black-box transferability (83.74% TASR) and robustness to defenses in remote sensing classification.
Efficientnet: Rethinking model scaling for con- volutional neural networks,
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Meta-ensemble learning on diverse ICBHI data splits reaches 66.49% Score and improves generalization on two external datasets.
Higher face density causes monotonic performance degradation in models and acts as a domain shift, even under balanced sampling.
Unreal Robotics Lab integrates Unreal Engine rendering with MuJoCo physics to enable high-fidelity simulation for robotics perception, control, and benchmarking under diverse conditions.
LRD-Net achieves state-of-the-art cross-domain face forgery detection via a frequency-guided lightweight backbone and real-centered learning with only 2.63M parameters and substantially faster training and inference.
citing papers explorer
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Physically-Induced Atmospheric Adversarial Perturbations: Enhancing Transferability and Robustness in Remote Sensing Image Classification
FogFool creates fog-based adversarial perturbations using Perlin noise optimization to achieve high black-box transferability (83.74% TASR) and robustness to defenses in remote sensing classification.
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Meta-Ensemble Learning with Diverse Data Splits for Improved Respiratory Sound Classification
Meta-ensemble learning on diverse ICBHI data splits reaches 66.49% Score and improves generalization on two external datasets.
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Face Density as a Proxy for Data Complexity: Quantifying the Hardness of Instance Count
Higher face density causes monotonic performance degradation in models and acts as a domain shift, even under balanced sampling.
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Unreal Robotics Lab: A High-Fidelity Robotics Simulator with Advanced Physics and Rendering
Unreal Robotics Lab integrates Unreal Engine rendering with MuJoCo physics to enable high-fidelity simulation for robotics perception, control, and benchmarking under diverse conditions.
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LRD-Net: A Lightweight Real-Centered Detection Network for Cross-Domain Face Forgery Detection
LRD-Net achieves state-of-the-art cross-domain face forgery detection via a frequency-guided lightweight backbone and real-centered learning with only 2.63M parameters and substantially faster training and inference.