Large Language Models' success on text generation has also made them better at code generation and coding tasks.
Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers to be produced in the physical real world.
In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both training data augmentation and model performance assessments.
Machine Learning (ML) systems are vulnerable to adversarial examples, particularly those from query-based black-box attacks.
This work aims to address this gap by offering a theoretical characterization of the trade-off between detection and false positive rates for stateful defenses.
Such stateful defenses aim to defend against black-box attacks by tracking the query history and detecting and rejecting queries that are "similar" and thus preventing black-box attacks from finding useful gradients and making progress towards finding adversarial attacks within a reasonable query budget.
Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones.
D4 uses an ensemble of models over disjoint subsets of the frequency spectrum to significantly improve adversarial robustness.
Given the prevalence of ERM sample complexity bounds, our proposed framework enables machine learning practitioners to easily understand the convergence behavior of multicalibration error for a myriad of classifier architectures.
Recent years have seen a surge in the popularity of acoustics-enabled personal devices powered by machine learning.
We answer this question with an analytical and empirical exploration of recent face obfuscation systems.
As real-world images come in varying sizes, the machine learning model is part of a larger system that includes an upstream image scaling algorithm.
We implement and evaluate Face-Off to find that it deceives three commercial face recognition services from Microsoft, Amazon, and Face++.
Cryptography and Security
In test-time attacks an adversary crafts adversarial examples, which are specially crafted perturbations imperceptible to humans which, when added to an input example, force a machine learning model to misclassify the given input example.
and how to design a classification paradigm that leverages these invariances to improve the robustness accuracy trade-off?
Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies.