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.
Rrto: A high-performance transparent offloading system for model inference in mobile edge computing
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
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The extended dual-envelope NMPC enables smoother drifting convergence and cuts steady-state tracking errors in speed, sideslip angle, and yaw rate by 33%, 71%, and 31% respectively in hardware tests.
SL-FAC reduces communication in split learning via frequency-aware compression of activations and gradients while aiming to preserve training-critical information.
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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Dual-Envelope Constrained Nonlinear MPC for Distributed Drive Electric Vehicles Drifting Under Bounded Steering and Direct Yaw-Moment Control
The extended dual-envelope NMPC enables smoother drifting convergence and cuts steady-state tracking errors in speed, sideslip angle, and yaw rate by 33%, 71%, and 31% respectively in hardware tests.
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SL-FAC: A Communication-Efficient Split Learning Framework with Frequency-Aware Compression
SL-FAC reduces communication in split learning via frequency-aware compression of activations and gradients while aiming to preserve training-critical information.