Detect Fractures with a Neural Network - AIClub Project
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This document summarizes a fracture identification app that takes in x-rays of arm fractures and determines if they are broken or not using Python code. The code allows users to input x-rays and receives responses from an AI model trained on datasets of fractured and normal bone x-ray images, achieving 77.8% accuracy. To improve the model, adding more training images and expanding it to detect fractures in other body areas would be beneficial.
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Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Artificial Intelligence and Electronic Warfare
Artificial Intelligence and Electronic WarfarePapadakis K.-Cyber-Information Warfare Analyst & Cyber Defense/Security Consultant-Hellenic MoD
AI & Electronic WarfareAppSec PNW: Android and iOS Application Security with MobSF
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
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- 2. My app is a fracture identifier, and it takes in x-rays of arm fractures and determines if it’s broken or not. ← →
- 3. The python code allows the user to give input and gives a response to the user, and the AI utilizes the datasets with x-ray images of fractured/normal bones.
- 4. There were 31 lines of code in my program, and I used Repl.it
- 5. I got my image data from both Google Images and the Stanford MURA dataset. The accuracy of my AI was 77.8%
- 6. In order to improve the accuracy, I would probably want to add more images to the dataset. Also, in the future, I would like to be able to predict fractures in more areas of the body, not just in arms.