IStego100K

IStego100K: Large-scale Image Steganalysis Dataset, mixed with various steganographic algorithms, embedding rates, and quality factors.

This dataset is proposed in:

@inproceedings{yangzl2019IStego100K,
  title         =   {IStego100K: Large-scale Image Steganalysis Dataset},
  author        =   {Yang, Zhongliang and Wang, Ke and Ma, Sai and Huang, Yongfeng and Kang, Xiangui and Zhao, Xianfeng},
  booktitle     =   {International Workshop on Digital Watermarking},
  year          =   {2019},
  organization  =   {Springer}
}

Full PDF can be downloaded from arxiv

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Train Set

100,000 pairs of cover and stego images (200K in total), origin images were downloaded from Unsplash

Same-Source Test Set

Marked as SS-Test in the paper. 8104 images with cover/stego labels (not in pair), origin images were downloaded from Unsplash

Different-Source Test Set

Marked as DS-Test in the paper.10000 images with cover/stego labels (not in pair), origin images were shot on different mobile devices.

Note: The number of images is 11809 in the paper, but we removed some low quality images before uploading.

Steganographic Algorithms

We use the following steganographic algorithms for our dataset:

nsF5: J. Fridrich, T. Pevný, and J. Kodovský, Statistically undetectable JPEG steganography: Dead ends, challenges, and opportunities. In J. Dittmann and J. Fridrich, editors, Proceedings of the 9th ACM Multimedia & Security Workshop, pages 3–14, Dallas, TX, September 20–21, 2007. [code] [pdf]

J-UNIWARD: V. Holub, J. Fridrich, T. Denemark, Universal Distortion Function for Steganography in an Arbitrary Domain, EURASIP Journal on Information Security, (Section:SI: Revised Selected Papers of ACM IH and MMS 2013), 2014(1). [code] [pdf]

UERD: L. Guo, J. Ni, W. Su, C. Tang, and Y.Q. Shi. Using statistical image model for jpeg steganography: uniform embedding revisited. IEEE Transactions on Information Forensics & Security, 10(12), 2669-2680, 2015. [code] [pdf]

Steganalysis Algorithms

We apply the following steganalysis algorithms for dataset evaluation:

DCTR: V. Holub and J. Fridrich, Low Complexity Features for JPEG Steganalysis Using Undecimated DCT, IEEE Transactions on Information Forensics and Security, to appear. [code] [pdf]

GFR: X. Song, F. Liu, C. Yang, X. Luo and Y. Zhang, Steganalysis of Adaptive JPEG Steganography Using 2D Gabor Filters, Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security. ACM, 2015. [code] [pdf]

SRNet: M. Boroumand,M. Chen,and J. Fridrich. Deep Residual Network for Steganalysis of Digital Images, IEEE Transactions on Information Forensics and Security. PP. 1-1. 10.1109/TIFS.2018.2871749, 2018. [code] [pdf]

XuNet: G. Xu. Deep convolutional neural network to detect j-uniward, Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. ACM, 2017. [code] [pdf]

Overall Results

Dataset	Methods	Acc(%)	P(%)	R(%)	F1(%)
SS-Test	DCTR GFR SRNet XuNet	71.34 66.26 - -	79.72 69.58 - -	57.23 57.97 - -	66.63 63.25 - -
DS-Test	DCTR GFR SRNet XuNet	56.95 59.12 - -	55.50 61.61 - -	70.11 48.42 - -	61.95 54.22 - -

Note: We trained SRNet and XuNet on a single GPU (GTX 1080Ti), and found that they are hardly to converge on IStego100K.

Results for Different Steganography Algorithms

Test Set	Steganalysis	Steganography	Acc(%)	P(%)	R(%)	F1(%)
SS-Test	DCTR	UERD nsF5 J-uniward	71.77 84.44 57.73	79.75 85.10 67.58	58.36 83.51 29.71	67.40 84.30 41.27
SS-Test	GFR	UERD nsF5 J-uniward	68.47 71.61 58.81	71.34 72.72 62.91	61.75 69.18 42.92	66.20 70.91 51.02
DS-Test	DCTR	UERD nsF5 J-uniward	53.96 62.28 51.67	53.35 60.56 51.43	63.06 87.59 59.83	57.80 71.61 55.31
DS-Test	GFR	UERD nsF5 J-uniward	56.05 67.24 54.59	58.40 68.21 56.62	42.09 64.58 39.26	48.92 66.35 46.37

Results for Different Steganography Algorithms

Test Set	Steganalysis	Payload	Acc(%)	P(%)	R(%)	F1(%)
SS-Test	DCTR	0.1 0.2 0.3 0.4	58.55 71.43 76.30 79.55	67.84 80.19 82.22 83.74	32.51 56.90 67.11 73.35	43.96 66.57 73.90 78.20
SS-Test	GFR	0.1 0.2 0.3 0.4	55.87 63.51 70.83 75.71	59.40 67.98 72.04 74.89	37.10 51.08 67.89 76.75	45.67 58.33 69.95 72.05
DS-Test	DCTR	0.1 0.2 0.3 0.4	52.86 56.21 58.56 60.17	52.42 54.99 56.53 57.72	61.90 68.40 74.11 76.05	56.77 60.97 64.13 65.63
DS-Test	GFR	0.1 0.2 0.3 0.4	52.29 56.66 62.15 65.40	53.42 58.87 64.65 67.18	35.79 44.19 53.65 60.22	42.86 50.49 58.63 63.51

Results for Different Quality Factors on SS-Test

Steganalysis	QF	Acc(%)	P(%)	R(%)	F1(%)
DCTR	75 80 85 90 95	75.23 71.50 74.09 69.04 62.12	85.63 86.48 84.34 76.09 66.41	60.64 61.56 59.18 55.54 49.05	71.00 71.82 69.55 64.21 56.43
GFR	75 80 85 90 95	70.08 69.91 68.42 64.67 58.30	75.06 74.98 71.54 67.02 59.76	60.15 59.75 61.17 57.75 50.82	66.78 66.50 65.95 62.04 64.93

More Details

For more details such as pre-processing, data distribution, and steganalysis baselines, please take a look at the paper.

rabi3elbeji / IStego100K