What Is Tax Evasion? It’s a Crime
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Audio: speech-to-text transcription. Language: sentiment analysis and textual entailment. Reinforcement learning: boat racing. Malware: evading a malware classifier using RL. As of today there is no consensus in the community as to why that might be. A number of hypotheses exist.
Later an opposing theory emerged, pioneered by Goodfellow , arguing that in fact adversarial examples occurred due to too much linearity in modern machine learning and especially deep learning systems. Goodfellow argued that activations functions like ReLU and Sigmoid are basically straight lines in the middle where, it just so happens we prefer to keep our gradients to prevent them from exploding or vanishing. If you then add tiny perturbations to some of the inputs a few pixels here and there that accumulates into massive difference on the other end of the network and it spits out gibberish.
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The third and perhaps most commonly adopted hypothesis today is the tilted boundary. They also empirically debunk the previous two approaches, so if you are technical this paper is worth a read. Our eyes are just bad sensors.
Tax Evasion and Inequality
If the entire paper is too long — I highly recommend this 11min blog summary. That paper, btw, uniquely explains another really interesting phenomenon related to adversarial examples — their transferability. Previous work pointed out that adversarial example easily transfer between both different models and different datasets. Because the MIT paper points out that noise is actually a feature, it makes sense that other images in the image realm be they cats, dogs, hotdogs or not hotdogs all share it.
Fascinating stuff. The more they know about your model and how its built — the easier it is for them to mount an attack on it.
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These are hands down the most powerful, because the attacker can use their detailed understanding of how the model thinks its gradients to mathematically optimize the attack. This is also the technique that works best for attacking hardened models. The three most powerful gradient-based attacks as of today are:. Confidence score attacks use the outputted classification confidence to estimate the gradients of the model, and then perform similar smart optimization to gradient-based attacks above.
The three most powerful confidence-based attacks as of today are:. This makes the attack more dumb — but arguably more realistic how many public endpoints do you know of that output a classification score? The most powerful attack in this category remains the Boundary Attack.
Tax evasion and inequality | Microeconomic Insights
Surrogate model attacks are very similar to gradient-based attacks, except they require an extra step. An intense bullet hell spectacle. Teamwork in a class of its own.
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