StegGNN: Learning Graphical Representation for Image Steganography

arXiv:2609.32362 · cs.CV · Submitted 2026-09-26 · Read on arXiv

cs.CV

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/1609abhi/StegGNN

Terminology

• GNNs — 11× in this paper · explained in 2 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• AUC — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Adversarial Training — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Feed Forward Network — 3× in this paper
• Graph Construction — 3× in this paper · explained in 3 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Convolutional Neural Networks (CNNs) — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Convolution Neural Network — 1× in this paper
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Graph Convolution Network — 1× in this paper
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Over-smoothing — 1× in this paper · explained in 4 episodes
• Over-squashing — 1× in this paper · explained in 3 episodes
• Peak Signal-to-Noise Ratio — 1× in this paper
• Point Clouds — 1× in this paper · explained in 2 episodes
• Sparse Attention — 1× in this paper · explained in 5 episodes

Sources

• StegExpose - A Tool for Detecting LSB Steganography
• NICE: Non-linear Independent Components Estimation
• Adam: A Method for Stochastic Optimization
• WiGNet: Windowed Vision Graph Neural Network

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