Adversarial Training for Object Detection Robustness

The patent applies preliminary action by performing adversarial training before deployment. Adversarial examples are generated and used to train the object detection model in advance, so that when the model encounters adversarial inputs during actual use, it has already been exposed to similar attacks and developed resistance. This preliminary exposure to adversarial conditions resolves the contradiction by building robustness beforehand without requiring complex runtime defenses.

The patent applies the skipping principle by using efficient adversarial attack algorithms that can quickly generate adversarial examples without requiring excessive computational resources. The method uses projected gradient descent (PGD) with a limited number of steps and early stopping criteria, allowing the adversarial training process to rush through the necessary iterations without wasting excessive time. This resolves the contradiction by achieving sufficient robustness through streamlined, time-efficient adversarial example generation.

The patent applies parameter changes by carefully adjusting the attack strength parameter (epsilon) and the number of attack steps during adversarial training. By modifying these parameters, the system finds an optimal balance where adversarial examples are strong enough to improve robustness but not so strong that they cause overfitting or degrade performance on clean images. This resolves the contradiction by dynamically tuning attack parameters to achieve both robustness and maintained detection accuracy.