153 research outputs found
The Empirical Models to Correct Water Column Effects for Optically Shallow Water
Seagrass as one of the blue carbon sinks plays an important role in environment, and it can be tracked remotely in the optically shallow water. Usually the signals of seagrass are weak which can be confused with the water column. The chapter will offer a model to simulate the propagation of light. The model can be used to improve the accuracy of seagrass mapping. Based on the in situ data, we found that the appropriate wavebands for seagrass mapping generally lie between 500–630 nm and 680–710 nm as well. In addition, a strong relationship between the reflectance value at 715 nm and LAI was found with a correlation coefficient of 0.99. The chapter provided an improved algorithm to retrieve bottom reflectance and map the bottom types. That would be meaningful for management and preservation of coastal marine resources
Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction
Federated learning is known for its capability to safeguard participants'
data privacy. However, recently emerged model inversion attacks (MIAs) have
shown that a malicious parameter server can reconstruct individual users' local
data samples through model updates. The state-of-the-art attacks either rely on
computation-intensive search-based optimization processes to recover each input
batch, making scaling difficult, or they involve the malicious parameter server
adding extra modules before the global model architecture, rendering the
attacks too conspicuous and easily detectable.
To overcome these limitations, we propose Scale-MIA, a novel MIA capable of
efficiently and accurately recovering training samples of clients from the
aggregated updates, even when the system is under the protection of a robust
secure aggregation protocol. Unlike existing approaches treating models as
black boxes, Scale-MIA recognizes the importance of the intricate architecture
and inner workings of machine learning models. It identifies the latent space
as the critical layer for breaching privacy and decomposes the complex recovery
task into an innovative two-step process to reduce computation complexity. The
first step involves reconstructing the latent space representations (LSRs) from
the aggregated model updates using a closed-form inversion mechanism,
leveraging specially crafted adversarial linear layers. In the second step, the
whole input batches are recovered from the LSRs by feeding them into a
fine-tuned generative decoder.
We implemented Scale-MIA on multiple commonly used machine learning models
and conducted comprehensive experiments across various settings. The results
demonstrate that Scale-MIA achieves excellent recovery performance on different
datasets, exhibiting high reconstruction rates, accuracy, and attack efficiency
on a larger scale compared to state-of-the-art MIAs
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