A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models
Machine learning models present a risk of adversarial attack when deployed in production. Quantifying the contributing factors and uncertainties using empirical measures could assist the industry with assessing the risk of downloading and deploying common model types. This work proposes modifying the traditional Drake Equation's formalism to estimate the number of potentially successful adversarial attacks on a deployed model. The Drake Equation is famously used for parameterizing uncertainties and it has been used in many research fields outside of its original intentions to estimate the number of radio-capable extra-terrestrial civilizations. While previous work has outlined methods for discovering vulnerabilities in public model architectures, the proposed equation seeks to provide a semi-quantitative benchmark for evaluating and estimating the potential risk factors for adversarial attacks.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackBIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Bringing PDEs to JAX with forward and reverse modes automatic differentiation
Partial differential equations (PDEs) are used to describe a variety of physical phenomena. Often these equations do not have analytical solutions and numerical approximations are used instead. One of the common methods …
On the Convergence of Modified Policy Iteration in Risk Sensitive Exponential Cost Markov Decision Processes
Modified policy iteration (MPI) is a dynamic programming algorithm that combines elements of policy iteration and value iteration. The convergence of MPI has been well studied in the context of discounted and average-cos…
Computational EfficiencyPhysics-driven machine learning models coupling PyTorch and Firedrake
Partial differential equations (PDEs) are central to describing and modelling complex physical systems that arise in many disciplines across science and engineering. However, in many realistic applications PDE modelling …
Inductive BiasDrake: An Efficient Executive for Temporal Plans with Choice
This work presents Drake, a dynamic executive for temporal plans with choice. Dynamic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior…
ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies
Empirical adversarial risk minimization (EARM) is a widely used mathematical framework to robustly train deep neural nets (DNNs) that are resistant to adversarial attacks. However, both natural and robust accuracies, in …
Adversarial AttackAdversarial Defense