Search Results for author: Dvijotham

Found 7 papers, 2 papers with code

(Certified!!) Adversarial Robustness for Free!

no code implementations21 Jun 2022 Nicholas Carlini, Florian Tramer, Krishnamurthy, Dvijotham, J. Zico Kolter

In this paper we show how to achieve state-of-the-art certified adversarial robustness to 2-norm bounded perturbations by relying exclusively on off-the-shelf pretrained models.

Adversarial Robustness Denoising

Learning Optimal Conformal Classifiers

1 code implementation ICLR 2022 David Stutz, Krishnamurthy, Dvijotham, Ali Taylan Cemgil, Arnaud Doucet

However, using CP as a separate processing step after training prevents the underlying model from adapting to the prediction of confidence sets.

Medical Diagnosis

Towards transformation-resilient provenance detection of digital media

no code implementations14 Nov 2020 Jamie Hayes, Krishnamurthy, Dvijotham, Yutian Chen, Sander Dieleman, Pushmeet Kohli, Norman Casagrande

In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.

An efficient nonconvex reformulation of stagewise convex optimization problems

no code implementations NeurIPS 2020 Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli, Krishnamurthy, Dvijotham

We establish theoretical properties of the nonconvex formulation, showing that it is (almost) free of spurious local minima and has the same global optimum as the convex problem.

Verification of Non-Linear Specifications for Neural Networks

no code implementations ICLR 2019 Chongli Qin, Krishnamurthy, Dvijotham, Brendan O'Donoghue, Rudy Bunel, Robert Stanforth, Sven Gowal, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli

We show that a number of important properties of interest can be modeled within this class, including conservation of energy in a learned dynamics model of a physical system; semantic consistency of a classifier's output labels under adversarial perturbations and bounding errors in a system that predicts the summation of handwritten digits.

A Dual Approach to Scalable Verification of Deep Networks

2 code implementations17 Mar 2018 Krishnamurthy, Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, Pushmeet Kohli

In contrast, our framework applies to a general class of activation functions and specifications on neural network inputs and outputs.

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