Recent research has brought to light the vulnerability of deep neural networks (DNNs) to adversarial examples. While several methods have been proposed for generating physical adversarial examples, they often suffer from a critical flaw -conspicuous and easily detectable patterns by humans, limiting their real-world effectiveness. To overcome this limitation, we introduce an innovative approach termed "dual adversarial camouflage" (DAC) that generates natural adversarial camouflage in the physical world. Our DAC method leverages natural styles to hide attacks effectively. The process involves a two-stage training process. In the first stage, we learn the style features from style images. Building on this, the second stage optimizes the camouflage obtained in the first stage by minimizing the target detection score, thus significantly enhancing the attack performance. Experiment results show that the adversarial camouflage generated by our method has high naturalness and can effectively deceive object detectors. In practical tests, the attack success rate of our adversarial camouflage in both the digital and physical worlds is impressive, achieving 96.9% and 80% respectively. This showcases the real-world potential and robustness of our DAC method in evading detection.