Towards compression-resistant privacy-preserving photo sharing on social networks
Zhibo Wang, Hengchang Guo, Zhifei Zhang, Mengkai Song, Siyan Zheng, Qian Wang, Ben Niu · 2020
The massive photos shared through the social networks nowadays, e.g., Facebook and Instagram, have aided malicious entities to snoop private information, especially by utilizing deep neural networks (DNNs) to learn from those personal photos. To protect photo privacy against DNNs, recent advances adopting adversarial examples could successfully fool DNNs. However, they are sensitive to those image compression methods that are commonly used on social networks to reduce transmission bandwidth or storage space. A recent work proposed to resist JPEG compression, while the compression methods adopted in social networks are black boxes, and variation of compression methods would significantly degrade the resistance.