Digital Image Steganographer Identification: A Comprehensive Survey
Source abstract
Abstract
The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse. Identifying steganographer is essential in tracing secret information origins and pre... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Steganography and Watermarking Techniques · Digital Media Forensic Detection · Chaos-based Image/Signal Encryption
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (98)
Show source-provided references
1. Quaternary quantized gaussian modulation with optimal polarity map selection for JPEG steganography10.1109/TIFS.2023.3303715 ↗
2. Audio steganography based on iterative adversarial attacks against convolutional neural networks10.1109/TIFS.2019.2963764 ↗
3. Adaptive QIM with minimum embedding cost for robust video steganography on social networks10.1109/TIFS.2022.3215901 ↗
4. Linguistic steganalysis via densely connected LSTM with feature pyramid
5. Steganography, cryptography, watermarking: A comparative study
6. Wiseman · Technical Report DS-2017-4 · 2017
7. Application of perfectly undetectable network steganography method for malware hidden communication
8. Linguistic steganalysis toward social network10.1109/TIFS.2022.3226909 ↗
9. Adaptive steganalysis based on statistical model of quantized DCT coefficients for JPEG images10.1109/TDSC.2019.2962672 ↗
10. An effective linguistic steganalysis framework based on hierarchical mutual learning10.1016/j.ins.2021.11.086 ↗
11. A universal audio steganalysis scheme based on multiscale spectrograms and DeepResNet10.1109/TDSC.2022.3141121 ↗
12. Universal deep network for steganalysis of color image based on channel representation10.1109/TIFS.2022.3196265 ↗
13. PixelSteganalysis: Pixel-wise hidden information removal with low visual degradation10.1109/TDSC.2021.3132987 ↗
14. Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths10.1016/j.dcan.2021.09.005 ↗
15. Recent advances in image-based steganalysis research10.3724/SP.J.1016.2009.01247 ↗
16. Batch steganography via generative network10.1109/TCSVT.2020.2974884 ↗
17. Introduction to deep learning
18. Cover reproducible steganography via deep generative models10.1109/TDSC.2022.3217569 ↗
19. An automatic cost learning framework for image steganography using deep reinforcement learning10.1109/TIFS.2020.3025438 ↗
20. Large-capacity and flexible video steganography via invertible neural network
21. Steganography embedding cost learning with generative multi-adversarial network10.1109/TIFS.2023.3318939 ↗
22. J. Zhu, “Passive defense against 3D adversarial point clouds through the lens of 3D steganalysis,” 2022, arXiv:2205.08738v1.
23. Adaptive feature selection for image steganalysis based on classification metrics10.1016/j.ins.2023.118973 ↗
24. Extraction method of secret message based on optimal hypothesis test10.1109/TDSC.2023.3243907 ↗
25. Steganographer detection via multi-scale embedding probability estimation10.1145/3352691 ↗
26. A combination of active and passive video steganalysis to fight sensitive data exfiltration through online video
27. Destroying robust steganography in online social networks10.1016/j.ins.2021.10.023 ↗
28. Image sanitization in online social networks: A general framework for breaking robust information hiding10.1109/TCSVT.2022.3224243 ↗
29. Diversity-based cascade filters for JPEG steganalysis10.1109/TCSVT.2019.2891778 ↗
30. A new approach for JPEG steganalysis with a cognitive evolving ensembler and robust feature selection10.1007/s12559-022-10087-3 ↗
31. Information theoretic steganalysis of processed image LSB steganography10.1007/s11042-022-13931-8 ↗
32. Structural design of convolutional neural networks for steganalysis10.1109/LSP.2016.2548421 ↗
33. Deep learning hierarchical representations for image steganalysis10.1109/TIFS.2017.2710946 ↗
34. Deep residual network for steganalysis of digital images10.1109/TIFS.2018.2871749 ↗
35. Identifying a steganographer in realistic and heterogeneous data sets
36. Moving steganography and steganalysis from the laboratory into the real world
37. Pooled steganalysis in JPEG: How to deal with the spreading strategy
38. A review of image steganalysis techniques for digital forensics
39. Digital image steganalysis: A survey on paradigm shift from machine learning to deep learning-based techniques10.1049/ipr2.12043 ↗
40. Deep learning for real-time image steganalysis: A survey10.1007/s11554-019-00915-5 ↗
41. The infinite race between steganography and steganalysis in images10.1016/j.sigpro.2022.108711 ↗
42. Batch steganography and pooled steganalysis
43. Practical strategies for content-adaptive batch steganography and pooled steganalysis
44. Adversarial batch image steganography against CNN-based pooled steganalysis10.1016/j.sigpro.2020.107920 ↗
45. LOF: Identifying density-based local outliers
46. T. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” 2016, arXiv:1609.02907.
47. CNN-based adversarial embedding for image steganography10.1109/TIFS.2019.2891237 ↗
48. Efficient JPEG batch steganography using intrinsic energy of image contents10.1109/TIFS.2021.3109464 ↗
49. Exploring abnormal behavior in swarm: Identify user using adversarial examples10.1109/TETCI.2022.3201294 ↗
50. Curvelet transform and cover selection for secure steganography10.1007/s11042-017-4706-x ↗
51. Break our steganographic system: The ins and outs of organizing boss
52. The first bows contest: Break our watermarking system
53. The steganographer is the outlier: Realistic large-scale steganalysis10.1109/TIFS.2014.2336380 ↗
54. Steganalysis over large-scale social networks with high-order joint features and clustering ensembles10.1109/TIFS.2015.2496910 ↗
55. Side channel steganalysis: When behavior is considered in steganographer detection10.1007/s11042-018-6582-4 ↗
56. Steganographic security analysis from side channel steganalysis and its complementary attacks10.1109/TMM.2019.2959909 ↗
57. Steganographer detection via enhancement-aware graph convolutional network
58. Steganographer detection via a similarity accumulation graph convolutional network10.1016/j.neunet.2020.12.026 ↗
59. Russell · Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More · 2013 · 137
60. The MNIST database of handwritten digit images for machine learning research10.1109/MSP.2012.2211477 ↗
61. A. Krizhevsky, “Learning multiple layers of features from tiny images,” 2009. Accessed: Aug. 31, 2024. [Online]. Available: https://www.cs.utoronto.ca/~kriz/learning-features-2009-TR.pdf
62. Rich models for steganalysis of digital images10.1109/TIFS.2012.2190402 ↗
63. Steganalysis of adaptive JPEG steganography using 2D Gabor filters
64. Distribution-preserving-based automatic data augmentation for deep image steganalysis10.1109/TMM.2021.3119994 ↗
65. A new paradigm for steganalysis via clustering
66. Multiclass detector of current steganographic methods for JPEG format10.1109/TIFS.2008.2002936 ↗
67. Robust ML model ensembles via riskdriven anti-clustering of training data10.1016/j.ins.2023.03.085 ↗
68. Content-adaptive steganography by minimizing statistical detectability10.1109/TIFS.2015.2486744 ↗
69. A new rule for cost reassignment in adaptive steganography10.1109/TIFS.2017.2718480 ↗
70. Efficient steganographer detection over social networks with sampling reconstruction10.1007/s12083-017-0603-3 ↗
71. Steganographer detection via deep residual network
72. Steganalysis via deep residual network
73. Iterative multi-order feature alignment for JPEG mismatched steganalysis10.1016/j.neucom.2016.06.037 ↗
74. Subsequent embedding in targeted image steganalysis: Theoretical framework and practical applications10.1109/TDSC.2022.3154967 ↗
75. Steganographer detection based on multiclass dilated residual networks
76. Steganalysis features for content-adaptive JPEG steganography10.1109/TIFS.2016.2555281 ↗
77. Content-adaptive selective steganographer detection via embedding probability estimation deep networks10.1016/j.neucom.2019.07.068 ↗
78. Fully convolutional networks for semantic segmentation
79. Non-local deep features for salient object detection
80. Deep clustering network for steganographer detection using latent features extracted from a novel convolutional autoencoder10.1007/s11063-022-10992-6 ↗
81. Approach to the selection of the best cover image for information embedding in JPEG images based on the principles of the optimality10.1080/12460125.2018.1460163 ↗
82. Cover selection steganography method based on similarity of image blocks
83. Towards improved steganalysis: When cover selection is used in steganography10.1109/ACCESS.2019.2955113 ↗
84. Cover selection for steganographic embedding
85. Cover selection for steganography using image similarity
86. Steganalysis of JPEG images: Breaking the F5 algorithm
87. Calibration revisited
88. Low-complexity features for JPEG steganalysis using undecimated DCT10.1109/TIFS.2014.2364918 ↗
89. Phase-aware projection model for steganalysis of JPEG images
90. The challenges of rich features in universal steganalysis
91. Steganalysis feature selection with multidimensional evaluation & dynamic threshold allocation10.1109/TCSVT.2023.3295364 ↗
92. Steganographer identification of JPEG image based on feature selection and graph convolutional representation
93. H. Wu, “Feature bagging for steganographer identification,” 2018, arXiv:1810.11973.
94. Feature bagging for outlier detection
95. MSCNN: Steganographer detection based on multi-scale convolutional neural networks
96. ImageNet classification with deep convolutional neural networks
97. Steganographer detection via multiple-instance learning graph convolutional networks
98. Optimizing pooling function for pooled steganalysis