A new cost function for spatial image steganography

By Bin Li, Ming Wang, Jiwu Huang, Xiaolong Li. International Conference on Information Photonics, 2014

624 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ceb6603c9e45f6b66c3a3cec09a5b4e64856a1fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Bin Li
2. Jiwu Huang
3. Mo Chen
4. Mehdi Boroumand
5. Gongshen Liu
6. Feng Zhu
7. Ru Zhang
8. Jianyi Liu

Citations in the corpus

• Deep residual learning for image steganalysis (2018, methodology): “Four states of the art steganographic algorithms, including the Wavelet ObtainedWeights steganography (WOW) [14], S-UNIWARD [13], HILL [19] and MiPOD [29], are used for evaluating the effectiveness of steganalytic algorithms.”
• SteganoGAN: High Capacity Image Steganography with GANs (2019, methodology): “Finally, we compare the detection error for images generated by STEGANOGAN against those reported by (Ye et al., 2017) on images generated by three state-of-the-art steganography algorithms: WOW (Holub & Fridrich, 2012), S-UNIWARD (Holub et al., 2014), and HILL (Li et al., 2014).”
• A New Payload Partition Strategy in Color Image Steganography (2020, methodology): “In the following experiments, WOW [7], S-UNIWARD [8] and HILL [9] are combined with the proposed ACMP strategy to evaluate the performance.”
• Deep Residual Network for Steganalysis of Digital Images (2019, methodology): “5 bpp (bits per pixel) for WOW [28], HILL [41], and S-UNIWARD [31].”
• Depth-Wise Separable Convolutions and Multi-Level Pooling for an Efficient Spatial CNN-Based Steganalysis (2020, methodology): “We note that Zhu-Net achieves significant improvements and best results compared with other CNN-based networks on different datasets against various steganography algorithms (The only exception is for HILL for the payload 0.1 bpp when ZhuNet performs by 0.4% worse than the SRNet).”
• Automatic Steganographic Distortion Learning Using a Generative Adversarial Network (2017, methodology): “= (ρi,j )H×W is heuristically designed in a content-adaptive manner in all of the state-of-the-art spatial steganographic schemes, including HUGO [20], S-UNIWARD [21], MiPOD [22], and HILL [23].”