Editing Steganography (section)

=== Steganography in streaming media ===
Since the era of evolving network applications, steganography research has shifted from image steganography to steganography in streaming media such as [[Voice over Internet Protocol]] (VoIP).

In 2003, Giannoula et al. developed a data hiding technique leading to compressed forms of source video signals on a frame-by-frame basis.<ref>{{Cite book|last1=Giannoula|first1=A.|last2=Hatzinakos|first2=D.|title=Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429) |chapter=Compressive data hiding for video signals |volume=1|pages=I–529–32|publisher=IEEE|doi=10.1109/icip.2003.1247015|isbn=0780377508|year=2003|s2cid=361883}}</ref>

In 2005, Dittmann et al. studied steganography and watermarking of multimedia contents such as VoIP.<ref>{{Cite journal|last1=Dittmann|first1=Jana|last2=Hesse|first2=Danny|last3=Hillert|first3=Reyk|date=21 March 2005|title=Steganography and steganalysis in voice-over IP scenarios: operational aspects and first experiences with a new steganalysis tool set|journal=Security, Steganography, and Watermarking of Multimedia Contents VII|volume=5681|pages=607|publisher=SPIE|doi=10.1117/12.586579|bibcode=2005SPIE.5681..607D|s2cid=206413447|editor1-last=Delp Iii|editor1-first=Edward J|editor2-last=Wong|editor2-first=Ping W}}</ref>

In 2008, Yongfeng Huang and Shanyu Tang presented a novel approach to information hiding in low bit-rate VoIP speech stream, and their published work on steganography is the first-ever effort to improve the codebook partition by using Graph theory along with Quantization Index Modulation in low bit-rate streaming media.<ref>B. Xiao, Y. Huang, and S. Tang, "An Approach to Information Hiding in Low Bit-Rate Speech Stream", in ''IEEE GLOBECOM 2008'', IEEE, pp. 371–375, 2008. {{ISBN|978-1-4244-2324-8}}.</ref>

In 2011 and 2012, Yongfeng Huang and Shanyu Tang devised new steganographic algorithms that use codec parameters as cover object to realise real-time covert VoIP steganography. Their findings were published in ''IEEE Transactions on Information Forensics and Security''.<ref>{{Cite journal|last1=Huang|first1=Yong Feng|last2=Tang|first2=Shanyu|last3=Yuan|first3=Jian|date=June 2011|title=Steganography in Inactive Frames of VoIP Streams Encoded by Source Codec|journal=IEEE Transactions on Information Forensics and Security|volume=6|issue=2|pages=296–306|doi=10.1109/tifs.2011.2108649|s2cid=15096702|issn=1556-6013|url=https://repository.uwl.ac.uk/id/eprint/3935/1/Steganography%20in%20inactive%20frames%20of%20VoIP%20streams%20encoded%20by%20source%20codec.pdf}}</ref><ref>{{Cite journal|last1=Huang|first1=Yongfeng|last2=Liu|first2=Chenghao|last3=Tang|first3=Shanyu|last4=Bai|first4=Sen|date=December 2012|title=Steganography Integration Into a Low-Bit Rate Speech Codec|journal=IEEE Transactions on Information Forensics and Security|volume=7|issue=6|pages=1865–1875|doi=10.1109/tifs.2012.2218599|s2cid=16539562|issn=1556-6013|url=https://repository.uwl.ac.uk/id/eprint/3932/1/Steganography%20Integration%20into%20a%20low-bit%20rate%20speech%20codec.pdf}}</ref><ref>{{Cite journal|title=Application of Lah transform for security and privacy of data through information hiding in telecommunication|journal=Transactions on Emerging Telecommunications Technologies|year=2020|doi=10.1002/ett.3984|last1=Ghosal|first1=Sudipta Kr|last2=Mukhopadhyay|first2=Souradeep|last3=Hossain|first3=Sabbir|last4=Sarkar|first4=Ram|volume=32|issue=2|s2cid=225866797}}</ref>

In 2024, Cheddad & Cheddad proposed a new framework <ref>{{Cite book|last1=Cheddad|first1= Zohra Adila|last2=Cheddad|first2=Abbas|title=Proceedings 2023 Intelligent Systems Conference (IntelliSys'23)|chapter= Active Restoration of Lost Audio Signals Using Machine Learning and Latent Information |series= Lecture Notes in Networks and Systems|volume= 822|publisher=LNCS,Springer|doi=10.1007/978-3-031-47721-8_1|year=2024 |pages= 1–16|isbn= 978-3-031-47720-1}}</ref> for reconstructing lost or corrupted audio signals using a combination of machine learning techniques and latent information. The main idea of their paper is to enhance audio signal reconstruction by fusing steganography, halftoning (dithering), and state-of-the-art shallow and deep learning methods (e.g., RF, LSTM). This combination of steganography, halftoning, and machine learning for audio signal reconstruction may inspire further research in optimizing this approach or applying it to other domains, such as image reconstruction (i.e., inpainting).