Meta Security Metric Learning for Secure Deep Image Hiding

Document Type

Article

Publication Date

1-1-2024

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.

Identifier

85187249625 (Scopus)

Publication Title

IEEE Transactions on Dependable and Secure Computing

External Full Text Location

https://doi.org/10.1109/TDSC.2024.3363692

e-ISSN

19410018

ISSN

15455971

First Page

4907

Last Page

4920

Issue

5

Volume

21

Grant

61972205

Fund Ref

National Natural Science Foundation of China

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