Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples. These examples make deployment of such models in safety-critical tasks questionable.Use of deep neural networks as inverse problem solvers has generated much excitement for medical imaging including CT and MRI, but recently a similar vulnerability has also been demonstrated for these tasks. We show that for such inverse problem solvers, one should analyze and study the effect of adversaries in the measurement-space,instead of the signal-space as in previous work. Read More
Daily Archives: July 10, 2020
Does Adversarial Transferability Indicate Knowledge Transferability?
Despite the immense success that deep neural networks (DNNs) have achieved,adversarial examples, which are perturbed inputs that aim to mislead DNNs to make mistakes, have recently led to great concern. On the other hand, adversarial examples exhibit interesting phenomena, such as adversarial transferability. DNNs also exhibit knowledge transfer, which is critical to improving learning efficiency and learning in domains that lack high-quality training data. In this paper, we aim to turn the existence and pervasiveness of adversarial examples into an advantage.Given that adversarial transferability is easy to measure while it can be challenging to estimate the effectiveness of knowledge transfer,does adversarial transferability indicate knowledge transferability? We first theoretically analyze the relationship between adversarial transferability and knowledge transferability and outline easily checkable sufficient conditions that identify when adversarial transferability indicates knowledge transferability. In particular, we show that composition with an affine function is sufficient to reduce the difference between two models when adversarial transferability between them is high. We provide empirical evaluation for different transfer learning scenarios on diverse datasets, including CIFAR-10,STL-10, CelebA, and Taskonomy-data – showing a strong positive correlation be-tween the adversarial transferability and knowledge transferability, thus illustrating that our theoretical insights are predictive of practice. Read More
#adversarialSystematic Literature Review to Investigate the Application of Open Source Intelligence (OSINT) with Artificial Intelligence
Open Source Intelligence (OSINT) is a concept to describe the search, collection, analysis, and use of information from open sources, as well as the techniques and tools used. OSINT emerges out of a military need to collect relevant and publicly available information. Through the use of OSINT, it is possible to find specific information that has some knowledge or provides an advantage. Since its emergence, some studies have been done proposing and developing new ways of using OSINT in different areas. In addition to OSINT, another field of study that has also been a worldwide trend and is being used together with other areas is Artificial Intelligence (AI). AI is the area of computer science responsible for the development of intelligent systems. However, a systematic literature review that investigates the use of OSINT over the years and your application with AI was not found. So, this work has an objective to develop a systematic literature review on OSINT to investigate the application of OSINT with AI. Read More
#icHow AI can empower communities and strengthen democracy
Each Fourth of July for the past five years I’ve written about AI with the potential to positively impact democratic societies. I return to this question in hopes of shining a light on technology that can strengthen communities, protect privacy and freedoms, and otherwise support the public good.
This series is grounded in the principle that artificial intelligence is capable of not just value extraction, but individual and societal empowerment. While AI solutions often propagate bias, they can also be used to detect that bias. As Dr. Safiya Noble has pointed out, artificial intelligence is one of the critical human rights issues of our lifetimes. AI literacy is also, as Microsoft CTO Kevin Scott asserted, a critical part of being an informed citizen in the 21st century. Read More
The US, China and the AI arms race: Cutting through the hype
The reality is that US and China efforts to develop AI are entwined, even if the tensions of coronavirus and trade disagreements may spur a separation.
… the narrative of an AI “arms race” between the US and China has been brewing for years. Dramatic headlines suggest that China is poised to take the lead in AI research and use, due to its national plan for AI domination and the billions of dollars the government has invested in the field, compared with the US’ focus on private-sector development.
But the reality is that at least until the past year or so, the two nations have been largely interdependent when it comes to this technology. It’s an area that has drawn attention and investment from major tech heavy hitters on both sides of the Pacific, including Apple, Google and Facebook in the US and SenseTime, Megvii and YITU Technology in China. Read More
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