Vol-Mark embeds watermarks into 3D medical volumes using contrastive learning for feature extraction and cubic difference expansion for embedding, achieving ACC above 0.90 against most attacks with reversible low-distortion properties.
A two-dimensional mapping with a strange attractor,
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QuChaTeR hybridizes chaotic maps and variational quantum circuits with recurrent networks and wavelets to achieve faster convergence and better performance than classical and quantum-inspired baselines on real seismic datasets.
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Vol-Mark: A Watermark for 3D Medical Volume Data Via Cubic Difference Expansion and Contrastive Learning
Vol-Mark embeds watermarks into 3D medical volumes using contrastive learning for feature extraction and cubic difference expansion for embedding, achieving ACC above 0.90 against most attacks with reversible low-distortion properties.
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QuChaTeR: A Hybrid Quantum-Chaotic Temporal Framework for Earthquake Prediction
QuChaTeR hybridizes chaotic maps and variational quantum circuits with recurrent networks and wavelets to achieve faster convergence and better performance than classical and quantum-inspired baselines on real seismic datasets.