The Adversarial Robustness Toolbox—securing AI against adversarial threats

Recent years have seen tremendous advances in the development of artificial intelligence (AI). Modern AI systems achieve human-level performance on cognitive tasks such as recognizing objects in images, annotating videos, converting speech to text, or translating between different languages. Many of these breakthrough results are based on Deep Neural Networks (DNNs). DNNs are complex machine learning models bearing certain similarity with the interconnected neurons in the human brain. DNNs are capable of dealing with high-dimensional inputs (e.g. millions of pixels in high-resolution images), representing patterns in those inputs at various levels of abstraction, and relating those representations to high-level semantic concepts.
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