CAT: A Conditional Adaptation Tailor for Efficient and Effective Instance-Specific Pansharpening on Real-World Data

Pansharpening is a crucial remote sensing technique that fuses low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images to generate high-resolution multispectral (HRMS) imagery. Although deep learning techniques have significantly advanced pansharpening, many existing methods suffer from limited cross-sensor generalization and high computational overhead, restricting their real-time applications. To address these challenges, we propose an efficient framework that quickly adapts to a specific input instance, completing both training and inference in a short time. Our framework splits the input image into multiple patches, selects a subset for unsupervised CAT training, and then performs inference on all patches, stitching them into the final output. The CAT module, integrated between the feature extraction and channel transformation stages of a pre-trained network, tailors the fused features and fixes the parameters for efficient inference, generating improved results. Our approach offers two key advantages: (1) $\textit{Improved Generalization Ability}$: by mitigating cross-sensor degradation, our model--although pre-trained on a specific dataset--achieves superior performance on datasets captured by other sensors; (2) $\textit{Enhanced Computational Efficiency}$: the CAT-enhanced network can swiftly adapt to the test sample using the single LRMS-PAN pair input, without requiring extensive large-scale data retraining. Experiments on the real-world data from WorldView-3 and WorldView-2 datasets demonstrate that our method achieves state-of-the-art performance on cross-sensor real-world data, while achieving both training and inference of $512\times512$ image within $\textit{0.4 seconds}$ and $4000\times4000$ image within $\textit{3 seconds}$ at the fastest setting on a commonly used RTX 3090 GPU.

MORSE-STF: A Privacy Preserving Computation System

Privacy-preserving machine learning has become a popular area of research due to the increasing concern over data privacy. One way to achieve privacy-preserving machine learning is to use secure multi-party computation, where multiple distrusting parties can perform computations on data without revealing the data itself. We present Secure-TF, a privacy-preserving machine learning framework based on MPC. Our framework is able to support widely-used machine learning models such as logistic regression, fully-connected neural network, and convolutional neural network. We propose novel cryptographic protocols that has lower round complexity and less communication for computing sigmoid, ReLU, conv2D and there derivatives. All are central building blocks for modern machine learning models. With our more efficient protocols, our system is able to outperform previous state-of-the-art privacy-preserving machine learning framework in the WAN setting.

MPC Protocol for G-module and its Application in Secure Compare and ReLU

Secure multi-party computation (MPC) is a subfield of cryptography. Its aim is creating methods for multiple parties to jointly compute a function over their inputs meanwhile keeping their inputs privately. The Secure Compare problem, introduced by Yao under the name millionaire's problem, is an important problem in MPC. On the other hand, Privacy Preserving Machine Learning (PPML) is an intersectional field of cryptography and machine learning. It allows a group of independent data owners to collaboratively learn a model over their data sets without exposing their private data. MPC is a common cryptographic technique commonly used in PPML. In Deep learning, ReLU is an important layer. In order to train neural network to use MPC, we need an MPC protocol for ReLU and DReLU (the derivative of ReLU) in forward propagation and backward propagation of neural network respectively. In this paper, we give two new tools "G-module action" and "G-module recover" for MPC protocol, and use them to give the protocols for Secure Compare, DReLU and ReLU. The total communication in online and offline of our protocols is much less than the state of the art.