828 research outputs found
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Deep neural networks have emerged as a widely used and effective means for
tackling complex, real-world problems. However, a major obstacle in applying
them to safety-critical systems is the great difficulty in providing formal
guarantees about their behavior. We present a novel, scalable, and efficient
technique for verifying properties of deep neural networks (or providing
counter-examples). The technique is based on the simplex method, extended to
handle the non-convex Rectified Linear Unit (ReLU) activation function, which
is a crucial ingredient in many modern neural networks. The verification
procedure tackles neural networks as a whole, without making any simplifying
assumptions. We evaluated our technique on a prototype deep neural network
implementation of the next-generation airborne collision avoidance system for
unmanned aircraft (ACAS Xu). Results show that our technique can successfully
prove properties of networks that are an order of magnitude larger than the
largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that
appeared at CAV 201
Toward Scalable Verification for Safety-Critical Deep Networks
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state of the art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification
SpecAttack: Specification-Based Adversarial Training for Deep Neural Networks
Safety specification-based adversarial training aims to generate examples
violating a formal safety specification and therefore provides approaches for
repair. The need for maintaining high prediction accuracy while ensuring the
save behavior remains challenging. Thus we present SpecAttack, a
query-efficient counter-example generation and repair method for deep neural
networks. Using SpecAttack allows specifying safety constraints on the model to
find inputs that violate these constraints. These violations are then used to
repair the neural network via re-training such that it becomes provably safe.
We evaluate SpecAttack's performance on the task of counter-example generation
and repair. Our experimental evaluation demonstrates that SpecAttack is in most
cases more query-efficient than comparable attacks, yields counter-examples of
higher quality, with its repair technique being more efficient, maintaining
higher functional correctness, and provably guaranteeing safety specification
compliance
- …