mlsanityData to 14 Oct 2026

StegNet: Mega Image Steganography Capacity with Deep Convolutional Network

arXiv 1806.06357 ↗First posted 17 Jun 2018cs.MMcited by 14

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Abstract (arXiv)

Traditional image steganography often leans interests towards safely embedding hidden information into cover images with payload capacity almost neglected. This paper combines recent deep convolutional neural network methods with image-into-image steganography. It successfully hides the same size images with a decoding rate of 98.2% or bpp (bits per pixel) of 23.57 by changing only 0.76% of the cover image on average. Our method directly learns end-to-end mappings between the cover image and the embedded image and between the hidden image and the decoded image. We~further show that our embedded image, while with mega payload capacity, is still robust to statistical analysis.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Jun 2018.

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Citing papers per year: 2021: 5; 2022: 3; 2023: 3; 2024: 0; 2025: 1; 2026: 2.

14 papers.

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