Meta Security Metric Learning for Secure Deep Image Hiding
Qi Cui, Weixuan Tang, Zhili Zhou, Ruohan Meng, Guoshun Nan, Yun-Qing Shi
Abstract
Deep Image Hiding (DIH) aims to imperceptibly hide images within image. To improve its security performance, some DIH methods design Security Metrics (SMs) to guide the learning of their hiding networks. However, these methods focus on optimizing their anti-steganalysis ability on specific SMs, resulting in inferior generalization ability. To overcome these limitations, in this paper, we introduce meta-learning into DIH and propose Meta Security Metric-based DIH (MSM-DIH). In the MSM-DIH, the Invertible Neural Network (INN)-based hiding network is learned under the guidance of a learnable meta SM generalized from multiple fixed source SMs, and each SM is composed of a metric network and a contrastive loss function. Specifically, MSM-DIH is trained with bi-level optimization. In the outer optimization, a meta SM is learned to assign higher security scores for more advanced stego images. Besides, the domain knowledge of steganalysis is transferred from the multiple pre-trained source metric networks to the meta metric network, so as to enhance the generalization ability of the meta SM. In the inner optimization, the hiding network is learned to generate more secure stego images according to the learned meta SM. Experimental results show that our MSM-DIH has achieved the best security performance in most cases.