Nowadays, privacy issue has become a top priority when training AI algorithms. Machine learning algorithms are expected to benefit our daily life, while personal information must also be carefully protected from exposure. Facial information is particularly sensitive in this regard. Multiple datasets containing facial information have been taken offline, and the community is actively seeking solutions to remedy the privacy issues. Existing methods for privacy preservation can be divided into blur-based and face replacement-based methods. Owing to the advantages of review convenience and good accessibility, blur-based based methods have become a dominant choice in practice. However, blur-based methods would inevitably introduce training artifacts harmful to the performance of downstream tasks. In this paper, we propose a novel De-artifact Blurring(DartBlur) privacy-preserving method, which capitalizes on a DNN architecture to generate blurred faces. DartBlur can effectively hide facial privacy information while detection artifacts are simultaneously suppressed. We have designed four training objectives that particularly aim to improve review convenience and maximize detection artifact suppression. We associate the algorithm with an adversarial training strategy with a second-order optimization pipeline. Experimental results demonstrate that DartBlur outperforms the existing face-replacement method from both perspectives of review convenience and accessibility, and also shows an exclusive advantage in suppressing the training artifact compared to traditional blur-based methods. Our implementation is available at https://github.com/JaNg2333/DartBlur.