Double phase modular steganography with the help of error images
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| 008 | 210128e20150701xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-013-1844-7 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-013-1844-7 | ||
| 245 | 0 | 0 | |a Double phase modular steganography with the help of error images |h [Elektronische Daten] |c [Masoud Afrakhteh, Inkyu Moon, Jeong-A Lee] |
| 520 | 3 | |a Many steganographic schemes based on popular least significant bit (LSB) embedding have been proposed. The embedding procedure is totally independent of pixel values due to the fact that only the LSB has to be altered. Other methods either randomly increment/decrement the pixel value or use a function to do so. Because of this, the maximum embedding capacity cannot exceed 1 bit per pixel (bpp). State-of-the-art steganographic schemes, such as edge adaptive (EA) and highly undetectable stego (HUGO), aim at such LSB-based approaches with the help of LSB-matching algorithms and, unlike classical steganography, are more concerned about the undetectability level of the stego image rather than the peak signal-to-noise ratio (PSNR) of the stego images. There is a trade-off between detectability and the embedding rate of the stego images. The larger the embedding rate, the higher the chance of detectability, and the opposite is also true. On the other hand, classical steganographic approaches aim for maximum embedding capacity (say 4 bpp) while their main ability is to provide greater PSNR values rather than undetectability. In this work, a novel modular steganographic scheme is proposed in a spatial domain that takes advantage of error images resulting from applying an image quality factor (the same as the ones used in JPEG compression) in order to find the pixels where the secret data could be embedded. We show that our proposed method is less detectable compared to the EA method. The proposed method is almost as undetectable as HUGO. The detectability level is evaluated by the most recent state-of-the-art steganalysis attack, called ensemble classifiers, with second-order subtractive pixel adjacency model features given as their input. In addition, the proposed method can embed up to 4 bpp, yet maintain a high PSNR value. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a JPEG image quality factors |2 nationallicence | |
| 690 | 7 | |a Error image |2 nationallicence | |
| 690 | 7 | |a Ensemble classifier |2 nationallicence | |
| 690 | 7 | |a Information hiding |2 nationallicence | |
| 690 | 7 | |a Steganography |2 nationallicence | |
| 700 | 1 | |a Afrakhteh |D Masoud |u Department of Computer Engineering, Chosun University, Gwangju, South Korea |4 aut | |
| 700 | 1 | |a Moon |D Inkyu |u Department of Computer Engineering, Chosun University, Gwangju, South Korea |4 aut | |
| 700 | 1 | |a Lee |D Jeong-A |u Department of Computer Engineering, Chosun University, Gwangju, South Korea |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/13(2015-07-01), 4833-4847 |x 1380-7501 |q 74:13<4833 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-013-1844-7 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11042-013-1844-7 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Afrakhteh |D Masoud |u Department of Computer Engineering, Chosun University, Gwangju, South Korea |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Moon |D Inkyu |u Department of Computer Engineering, Chosun University, Gwangju, South Korea |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Lee |D Jeong-A |u Department of Computer Engineering, Chosun University, Gwangju, South Korea |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/13(2015-07-01), 4833-4847 |x 1380-7501 |q 74:13<4833 |1 2015 |2 74 |o 11042 | ||