deep-pwning – Metasploit for Machine Learning

Deep-pwning is a light-weight framework for experimenting with device finding out fashions with the function of comparing their robustness in opposition to a motivated adversary.
Be aware that deep-pwning in its present state is no the place with regards to adulthood or finishing touch. It’s supposed to be experimented with, expanded upon, and prolonged through you. Simplest then are we able to lend a hand it in point of fact develop into the goto penetration trying out toolkit for statistical device finding out fashions.

Background
Researchers have discovered that it’s unusually trivial to trick a device finding out style (classifier, clusterer, regressor and so on.) into making an objectively improper choices. This box of analysis is named Adverse Machine Learning . It’s not hyperbole to assert that any motivated attacker can bypass any device finding out gadget, given sufficient data and time. Alternatively, this factor is incessantly lost sight of when architects and engineers design and construct device finding out programs. The effects are being concerned when those programs are put into use in crucial situations, corresponding to within the scientific, transportation, monetary, or safety-similar fields.
Therefore, when one is comparing the efficacy of packages the usage of device finding out, their malleability in an opposed environment must be measured along the gadget’s precision and recall.
This instrument used to be launched at DEF CON 24 in Las Vegas, August 2016, all through a chat titled Machine Duping 101: Pwning Deep Learning Programs .
Construction
This framework is constructed on most sensible of Tensorflow , and most of the integrated examples on this repository are changed Tensorflow examples bought from the Tensorflow GitHub repository .
The entire integrated examples and code enforce deep neural networks , however they are able to be used to generate opposed pictures for in a similar fashion tasked classifiers that aren’t applied with deep neural networks. That is on account of the phenomenon of ‘transferability’ in device finding out, which used to be Papernot et al. expounded expertly upon in this paper . This implies signifies that opposed samples crafted with a DNN style A might be able to idiot some other distinctly structured DNN style B , in addition to another SVM style C .
This determine taken from the aforementioned paper (Papernot et al.) presentations the share of a hit opposed misclassification for a supply style (used to generate the opposed pattern) on a goal style (upon which the opposed pattern is examined).

Parts
Deep-pwning is modularized into a number of parts to attenuate code repetition. On account of the massively other nature of attainable classification duties, the present iteration of the code is optimized for classifying pictures and words (the usage of phrase vectors).
Those are the code modules that make up the present iteration of Deep-pwning:

  1. Drivers
    The drivers are the primary execution level of the code. That is the place you’ll be able to tie the other modules and parts in combination, and the place you’ll be able to inject extra customizations into the opposed technology processes.
  2. Fashions
    That is the place the true device finding out style implementations are situated. As an example, the supplied lenet5 style definition is situated within the style() serve as witihn lenet5.py . It defines the community as the next:
      -> Enter
    -> Convolutional Layer 1
    -> Max Pooling Layer 1
    -> Convolutional Layer 2
    -> Max Pooling Layer 2
    -> Dropout Layer
    -> Softmax Layer
    -> Output

    LeCun et al. LeNet-5 Convolutional Neural Community

  3. Adverse (advgen)
    This module comprises the code that generates opposed output for the fashions. The run() serve as outlined in every of those advgen categories takes in an input_dict , that comprises a number of predefined tensor operations for the device finding out style outlined in Tensorflow. If the style that you’re producing the opposed pattern for is understood, the variables within the enter dict must be primarily based off that style definition. Else, if the style is unknown, (black field technology) a exchange style must be used/applied, and that style definition must be used. Variables that wish to be handed in are the enter tensor placeholder variables and labels (incessantly refered to as x -> enter and y_ -> labels), the style output (incessantly refered to as y_conv ), and the true take a look at information and labels that the opposed pictures shall be primarily based off of.
  4. Config
    Software configurations.
  5. Utils
    Miscellaneous utilities that do not belong anyplace else. Those come with helper purposes to learn information, care for Tensorflow queue inputs and so on.

Those are the useful resource directories related to the applying:

  1. Checkpoints
    Tensorflow lets you load a partly skilled style to renew coaching, or load an absolutely skilled style into the applying for analysis or acting different operations. These kind of stored ‘checkpoints’ are saved on this useful resource listing.
  2. Information
    This listing shops all of the enter information in no matter structure that the driving force utility takes in.
  3. Output
    That is the output listing for all utility output, together with opposed pictures which might be generated.

Getting Began

Set up
Please apply the instructions to put in tensorflow discovered right here https://www.tensorflow.org/variations/r0.8/get_started/os_setup.html which is able to help you pick out the tensorflow binary to put in.

$ pip set up -r necessities.txt

Execution Instance (with the MNIST driving force)
To revive from a in the past skilled checkpoint. (configuration in config/mnist.conf)

$ cd dpwn
$ python mnist_driver.py --restore_checkpoint

To coach from scratch. (be aware that any earlier checkpoint(s) situated within the folder specified within the configuration shall be overwritten)

$ cd dpwn
$ python mnist_driver.py

Process listing

  • Put in force saliency graph way of producing opposed samples
  • Upload protection module to the undertaking for examples of a few defenses proposed in literature
  • Improve to Tensorflow 0.9.0
  • Upload beef up for the usage of pretrained word2vec style in sentiment driving force
  • Upload SVM & Logistic Regression beef up in fashions (+ instance that makes use of them)
  • Upload non-symbol and non-word classifier instance
  • Upload multi-GPU coaching beef up for sooner coaching speeds

Necessities

Be aware that dpwn calls for Tensorflow 0.8.0. Tensorflow 0.9.0 introduces some

Contributing
(borrowed from the fantastic Requests repository through kennethreitz)

  • Test for open problems or open a recent factor to start out a dialogue round a function concept or a computer virus.
  • Fork the repository on GitHub to start out making your adjustments to the grasp department (or department off of it).
  • Write a take a look at which presentations that the computer virus used to be mounted or that the function works as anticipated.
  • Ship a pull request and insect the maintainer till it will get merged and printed. πŸ™‚ Be sure you upload your self to AUTHORS.md .

Acknowledgements
There may be such a lot spectacular paintings from such a lot of device finding out and safety researchers that without delay or not directly contributed to this undertaking, and impressed this framework. That is an inconclusive listing of assets that used to be used or referenced in one means or some other:

Papers

Code

Datasets

Marshmallow

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