SmoothAdversarialTraining | Smooth Adversarial Training | Cybersecurity library

 by   cihangxie Python Version: Current License: MIT

kandi X-RAY | SmoothAdversarialTraining Summary

kandi X-RAY | SmoothAdversarialTraining Summary

SmoothAdversarialTraining is a Python library typically used in Security, Cybersecurity, Pytorch applications. SmoothAdversarialTraining has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However SmoothAdversarialTraining build file is not available. You can download it from GitHub.

The widely-used ReLU activation function significantly weakens adversarial training due to its non-smooth nature. In this project, we developed smooth adversarial training (SAT), in which we replace ReLU with its smooth approximations (e.g., SILU, softplus, SmoothReLU) to strengthen adversarial training. On ResNet-50, the best result reported by SAT on ImageNet is 69.7% accuracy and 42.3% robustness, beating its ReLU version by 0.9% for accuracy and 9.3% for robustnes. We also explore the limits of SAT with larger networks. We obtain the best result by using EfficientNet-L1, which achieves 82.2% accuracy and 58.6% robustness on ImageNet.
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              SmoothAdversarialTraining has a low active ecosystem.
              It has 49 star(s) with 1 fork(s). There are 3 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of SmoothAdversarialTraining is current.

            kandi-Quality Quality

              SmoothAdversarialTraining has no bugs reported.

            kandi-Security Security

              SmoothAdversarialTraining has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              SmoothAdversarialTraining is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              SmoothAdversarialTraining releases are not available. You will need to build from source code and install.
              SmoothAdversarialTraining has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed SmoothAdversarialTraining and discovered the below as its top functions. This is intended to give you an instant insight into SmoothAdversarialTraining implemented functionality, and help decide if they suit your requirements.
            • Create a model function
            • Builds a model
            • Decodes a block_string
            • Decodes a list of blocks
            • Connects the graph
            • Round a set of filters
            • Calculates the number of repeat repeats
            • Call stem layers
            • Start training
            • Compute tower function
            • Call the convolution layer
            • Archives a checkpoint
            • Call the block
            • Runs inference on the image
            • Builds the graph
            • Build model base
            • Generate representative dataset
            • Exports model
            • Return a list of bigtable tables from the given flags
            • Calculate the sharpness of an image
            • Run inference on examples
            • Builds the network graph
            • Rotate image
            • Blend the grayscale
            • Encode the given blocks
            • Return an EvalCkptDriver instance
            Get all kandi verified functions for this library.

            SmoothAdversarialTraining Key Features

            No Key Features are available at this moment for SmoothAdversarialTraining.

            SmoothAdversarialTraining Examples and Code Snippets

            No Code Snippets are available at this moment for SmoothAdversarialTraining.

            Community Discussions

            QUESTION

            hardware based password manager integration with device
            Asked 2021-Apr-28 at 12:48

            I am aiming to build a hardware based password manager that will store credentials like -username and passwords- externally, right now I am searching about it but I am having trouble in identifying that how will that external device integrate with browsers and websites when connected to provide the credentials stored in it. I mean what technique is used to integrate the hardware password managers to the device or browser.

            I would appreciate any sort of help and guidance from your side, Thanks!

            ...

            ANSWER

            Answered 2021-Apr-28 at 12:48

            Usually they inject passwords using a HID device acting as a keyboard. Check out the OnlyKey as an example.

            The way these work is by injecting/typing username and password based on pressing a hardware button against which you have stored the relevant credentials. There is also the option to complete MFA by storing an OTP token. Some will act like any other password manager by parsing the website URL against what is stored, but I guess this opens an attack surface when feeding data back to the device.

            -- BVS

            Source https://stackoverflow.com/questions/67290550

            QUESTION

            What does "assumptions" refer to when writing a pentest report?
            Asked 2021-Apr-16 at 15:25

            I have to write the "assumptions" part of a pentest report and I am having trouble understanding what I should write. I checked multiple pentest reports (from https://github.com/juliocesarfort/public-pentesting-reports) but none of them had this paragraph.
            Also I found this explanation "In case there are some assumptions that the pen-tester considers before or during the test, the assumptions need to be clearly shown in the report. Providing the assumption will help the report audiences to understand why penetration testing followed a specific direction.", but still what I do have in mind it is more suited for "attack narative".
            Can you provide me a small example (for one action, situation) so I can see exactly how it should be written?

            ...

            ANSWER

            Answered 2021-Apr-16 at 15:25

            I would think the "assumptions" paragraph and the "Attack narrative" paragraph are somehow overlapping. I would use the "Assumptions" paragraph to state a couple of high level decisions made before starting the attack, with whatever little information the pentester would have on the attack. I would expand on the tools and techniques used in the "Attack narrative" paragraph

            For example an assumption could be: "The pentester is carrying on the exercise against the infrastructure of a soho company with less than 5 people It is common for soho companies to use consumer networking equipment that is usually unsecure, and left configured as defualt. For this reason the attacker focused on scanning for http and ssh using a database of vendors default username and passwords"

            Source https://stackoverflow.com/questions/67126985

            QUESTION

            Is there a way to use a particular C function/symbol as output by nm
            Asked 2021-Mar-10 at 23:13

            I'm trying to analyse a compiled file for cybersec learning purposes and want to use a particular function.

            Here is the output of nm --defined-only ./compiled_file:

            ...

            ANSWER

            Answered 2021-Mar-09 at 12:54

            Yes, it is possible. The point of having exported symbols in shared libraries is to be able to use them - after all. In C, you can do this either by linking the library to the application (not really an option for python), or runtime loading the library and finding the required symbol (on linux: dlopen, dlsym). The manpage example shows how to do this in C.

            Source https://stackoverflow.com/questions/66547182

            QUESTION

            How to allow XML, JSON and CSV files to be uploaded when CSP is set in the webpage
            Asked 2020-Nov-04 at 19:09

            Currently, I have set the following CSP header in the HTML file of my webpage -

            ...

            ANSWER

            Answered 2020-Nov-04 at 19:09

            The issue was caused and fixed as follows -

            The button that takes XML file as input in the HTML form has an inline event handler, which the CSP Policy was blocking, thereby blocking the upload. I moved this inline event handler to an external function and called the function. This fixed the issue and CSP is no longer blocking the function.

            Source https://stackoverflow.com/questions/64421818

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install SmoothAdversarialTraining

            You can download it from GitHub.
            You can use SmoothAdversarialTraining like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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