Deep Learning Security
Deep Learning Security - Related Videos
10:25
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Cited by 54 •
2022
This is a method for training a multi-slice U-Net for multi-class segmentation of cryo-electron tomograms using a portion of one tomogram as a training input. We describe how to infer this network to other tomograms and how to extract segmentations for further analyses, such as subtomogram averaging and filament tracing.
08:20
Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
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Cited by 7 •
2023
Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.
04:17
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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2024
We present a protocol that combines recombinase polymerase amplification with a CRISPR/Cas12a system for trace detection of DNA viruses and builds portable smartphone microscopy with an artificial intelligence-assisted classification for point-of-care DNA virus detection.
09:47
DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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Cited by 8 •
2023
DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.
01:18
Secured Loans
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2025
Secured loans play an important role in supporting businesses by providing access to substantial funding while minimizing risks for lenders. These loans require the borrower to pledge assets as collateral, such as property, equipment, or inventory, which acts as security for the loan. This reduces the lender's risk, often resulting in lower interest rates than unsecured loans. For businesses, secured loans are a reliable way to finance significant expenses or investments, such as purchasing...
FAQs
Threat modeling identifies how attacks could affect a neural-network system’s confidentiality, integrity, or reliability across training, software, data, and deployment. It helps engineers connect specific threats with appropriate safeguards, such as robust training for manipulated inputs, access controls for exposed interfaces, or monitoring for suspicious behavior. This prioritization supports security planning in systems with significant operational consequences.
Adversarial inputs target a model during use by introducing small, difficult-to-detect perturbations that can alter its output. Poisoned data acts earlier, changing what the model learns and potentially shifting its decision boundaries. Because the attacks affect different stages, defenses must address both model behavior at input time and the integrity of data used during training.
Exposed model interfaces can reveal information beyond the intended prediction service. Model extraction threatens confidentiality by enabling others to reproduce aspects of a protected model, while sensitive-data inference can disclose information associated with the model or its data. Access controls, monitoring, and security testing help engineers identify and reduce risks created by externally reachable interfaces.
Testing against adaptive attacks matters because an attacker can adjust tactics in response to observed defenses. A system that performs well against a fixed test may therefore remain vulnerable when the attack changes. Incorporating adaptive testing into security evaluation gives engineers a more realistic view of robustness and helps reveal weaknesses that ordinary testing might miss.
A security-engineering workflow can combine threat modeling, robust training, input validation, access controls, monitoring, and testing against adaptive attacks. These practices address different points in the system rather than relying on a single protective measure. Together, they support more reliable operation by limiting harmful inputs, protecting interfaces, identifying suspicious behavior, and evaluating resilience under attack.
Security becomes especially consequential when neural-network outputs influence autonomous systems, medical analysis, industrial control, or other safety-critical technologies. In these settings, compromised reliability or integrity can affect operational decisions, while confidentiality failures can expose sensitive information. Engineering safeguards therefore help preserve trustworthy performance throughout model training, software operation, and deployment.