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Adversarial robustness using clever hans

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Adversarial robustness using clever hans

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The increasing accuracy of the machine learning systems is quite impressive. It has naturally led to a veritable flood of applications using them including self-driving vehicles, face recognition, cancer diagnosis and even in next-gen shops. A few years ago, getting wrong predictions from a machine learning model used to be the norm. Nowadays, this has become the exception, and we’ve come to expect them to perform flawlessly, especially when they are deployed in real-world applications.

The increasing accuracy of the machine learning systems is quite impressive. It has naturally led to a veritable flood of applications using them including self-driving vehicles, face recognition, cancer diagnosis and even in next-gen shops. A few years ago, getting wrong predictions from a machine learning model used to be the norm. Nowadays, this has become the exception, and we’ve come to expect them to perform flawlessly, especially when they are deployed in real-world applications.

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Adversarial robustness using clever hans

  1. 1. ADVERSARIAL ROBUSTNESS USING CLEVER HANS
  2. 2. What is Adversarial? ❖ Adversarial AI is the malicious development. ❖ Adversarial examples are a stark reminders of how different artificial intelligence and the human mind are. ❖ Adversarial search is a game-playing technique where the agents are surrounded by a competitive environment. ❖ An adversarial attack is one in which inputs to a deep learning neural network ultimately result in unexpected outputs. 2
  3. 3. What is Robustness? ❖ Robustness is the ability of a computer system to cope with errors during execution and cope with erroneous input. ❖ Robustness can encompass many areas of computer science. ❖ The robustness is the property that characterizes how effective your algorithm is. ❖ The robust algorithm is the one, the testing error of which is close to the training error. 3
  4. 4. Introduction to Adversarial Robustness ❖ Adversarial robustness measures a network's resilience against adversarial inputs. ❖ Adversarial inputs that are produced by taking inputs that are correctly classified by the DNN. ❖ It is defined as the accuracy of well-trained models on the adversarial countermeasures of test data generated in white-box settings. 4
  5. 5. Adversarial Attacks 5 ❖ Adversarial examples are inputs to machine learning models designed to intentionally fool them. ❖ The main worrying attributes of adversarial attacks are: -Imperceptibility -Targeted Manipulation -Transferability -Lack of theoretical model
  6. 6. “ Can we really trust the decisions made by AI or ML models? To find that out we can do research using Adversarial Robustness. 6
  7. 7. EXAMPLE 7
  8. 8. Introduction to Clever Hans 8 ❖ A Python library to benchmark machine learning systems' vulnerability to Adversarial Examples. ❖ This library uses Jax, PyTorch or TensorFlow 2 to accelerate graph computations performed by many machine learning models. ❖ Once dependencies have been taken care of, you can install CleverHans using pip or by cloning the official Github repository.
  9. 9. -> pip installation: pip install cleverhans -> Clone the repository : Link -> Installation for Development: cd cleverhans pip install -e 9 INSTALLATION PROCESS
  10. 10. “ Adversarial Machine Learning and Using CleverHans to make your ML models Robust. 10
  11. 11. ATTACKS contains the implementations of adversarial example crafting algorithms CORE FUNCTIONALITIES Utils_tf contains helper functions to train and evaluate models using TensorFlow. 11
  12. 12. FAST GRADIENT SIGN METHOD 12
  13. 13. FAST GRADIENT SIGN METHOD 13
  14. 14. THANK YOU ⬢ SHRUTI MISHRA - 2GI18CS144 ⬢ SONAL DESHMUKH - 2GI18CS156 14

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