A Lightweight Image Forgery Prevention Scheme for IoT Using GAN-Based Steganography

Xiao Li, Liquan Chen, Ju Jia, Zhongyuan Qin, Zhangjie Fu

Abstract: Computer vision (CV) applications empower various Internet of Things (IoT) scenarios. However, their advancements in image generation and manipulation tools make it increasingly easy to produce highly deceptive forged images, escalating the risk of image forgery. Cryptography-based methods can secure images but cannot support direct CV applications with compromised visual legibility. Existing generative adversarial network (GAN)-based steganography methods can effectively facilitate CV applications and image forgery prevention with high indistinguishability between stego and cover images. However, they are inefficient in resource-constrained IoT scenarios. Therefore, we propose a lightweight image forgery prevention scheme for IoT using GAN-based steganography. Our scheme embeds identity data within images. If forged, it fails to recover, triggering alerts. Our scheme can significantly improve efficiency with a lightweight generator designed by incorporating blueprint separable convolutions, sum connections and discrete wavelet transform while ensuring high effectiveness. Real-world IoT experimental results demonstrate this.