New Concept of Defendability Against Backdoors in Machine Learning
/ 4 min read
Quick take - Researchers from the Alignment Research Center have introduced a formal concept of defendability against backdoors in machine learning models, framing it as a strategic game between an attacker and a defender, and highlighting its implications for model evaluation, detection mechanisms, and future research in AI alignment.
Fast Facts
- Researchers from the Alignment Research Center introduced a formal concept of defendability against backdoors in machine learning, framing it as a strategic game between an attacker and a defender.
- The defender’s goal is to detect a specific input trigger modified by the attacker, with a function class deemed defendable if the trigger can be identified with high probability.
- The paper connects defendability to learnability, noting that in computationally unbounded settings, defendability is determined by the Vapnik–Chervonenkis (VC) dimension, while efficient PAC learnability ensures efficient defendability in bounded settings.
- The authors highlight that polynomial size decision trees can be defended efficiently against backdoor attacks, contrasting with polynomial size circuits that may not be efficiently defendable under certain conditions.
- The study emphasizes the significance of the white-box setting for defenders and outlines future research directions, including the relationship between defense mechanisms and AI alignment.
New Formal Concept of Defendability Against Backdoors in Machine Learning
In a recent scholarly article, researchers from the Alignment Research Center have introduced a new formal concept of defendability against backdoors in machine learning models. The article is authored by Paul Christiano, Jacob Hilton, Victor Lecomte, and Mark Xu.
Strategic Game Between Attacker and Defender
The authors frame this concept as a strategic game between an attacker and a defender. In this scenario, the attacker modifies a machine learning function to exhibit different behavior on a specific input known as a “trigger,” while the function is designed to behave similarly on other inputs. The primary objective of the defender is to detect this trigger during the evaluation of the model. A function class is deemed defendable if the defender can successfully identify the trigger with high probability. The attacker is constrained to use a strategy that works for a randomly chosen trigger, which is a crucial factor in enabling effective defense mechanisms.
Relation to Learnability and Defendability
The authors relate the notion of defendability to the concept of learnability, although they do not explicitly reference learning within the context of their definitions. In scenarios where computational constraints are not a factor, the defendability of a model is determined by its Vapnik–Chervonenkis (VC) dimension, drawing parallels to Probably Approximately Correct (PAC) learnability. Conversely, in a computationally bounded setting, efficient PAC learnability guarantees efficient defendability, though the reverse implication does not hold true. The paper presents polynomial size circuits as a class that may not be efficiently defendable under certain cryptographic assumptions, while polynomial size decision trees are highlighted as an example where defense against backdoor attacks is more feasible than the learning process itself.
Contributions and Future Directions
The introduction of the paper discusses existing literature on backdoor attacks and defenses, pinpointing a notable gap in theoretical frameworks that accommodate arbitrary modifications of models. The focus of the study is primarily on the runtime detection of backdoors, which does not require defenders to identify a model as backdoored without specific input. The authors emphasize the significance of the white-box setting, where defenders have complete access to the model’s description. They outline their contributions, which include definitions of defendability, statistical defendability, computational defendability, and illustrative examples. Statistical defendability is characterized by the VC dimension, especially concerning computationally unbounded defenders, while efficient defendability is defined for polynomial-time defenders, with implications tied to PAC learnability.
The discussion extends to the defendability of decision trees, demonstrating that they can be defended efficiently under a uniform input distribution. The authors reference related work, including prior studies on backdoor attacks, digital signature schemes, and the inherent challenges associated with detecting backdoors in machine learning models. Towards the end of the paper, they propose future research directions, investigating the relationship between defense mechanisms and learning, as well as considering implications for AI alignment. The conclusion reiterates the importance of the newly introduced formal notion of defendability and emphasizes its potential applications in enhancing the understanding of backdoor defenses in machine learning systems.
Original Source: Read the Full Article Here
