| 000 | 03326nam a22005775i 4500 | ||
|---|---|---|---|
| 999 |
_c200458307 _d76519 |
||
| 003 | TR-AnTOB | ||
| 005 | 20231120100218.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220204s2022 si | s |||| 0|eng d | ||
| 020 | _a9789811690419 | ||
| 024 | 7 |
_a10.1007/978-981-16-9041-9 _2doi |
|
| 040 |
_aTR-AnTOB _beng _erda _cTR-AnTOB |
||
| 041 | _aeng | ||
| 050 | 4 | _aTA1637 | |
| 072 | 7 |
_aTJF _2bicssc |
|
| 072 | 7 |
_aUYS _2bicssc |
|
| 072 | 7 |
_aTEC008000 _2bisacsh |
|
| 072 | 7 |
_aTJF _2thema |
|
| 072 | 7 |
_aUYS _2thema |
|
| 090 | _aTA1637EBK | ||
| 100 | 1 |
_aSoni, Badal. _eauthor. _0(orcid)0000-0002-9617-9468 _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 245 | 1 | 0 |
_aImage Copy-Move Forgery Detection _h[electronic resource] : _bNew Tools and Techniques / _cby Badal Soni, Pradip K. Das. |
| 250 | _a1st ed. 2022. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
|
| 300 | _a1 online resource | ||
| 336 |
_atext _btxt _2rdacontent |
||
| 337 |
_acomputer _bc _2rdamedia |
||
| 338 |
_aonline resource _bcr _2rdacarrier |
||
| 347 |
_atext file _bPDF _2rda |
||
| 490 | 1 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v1017 |
|
| 505 | 0 | _aIntroduction -- Background Study and Analysis -- Copy-Move Forgery Detection using Local Binary Pattern Histogram Fourier Features -- Blur Invariant Block-based CMFD System using FWHT Features -- Geometric Transformation Invariant Improved Block based Copy-Move Forgery Detection -- Key-points based Enhanced Copy-Move Forgery Detection System using DBSCAN Clustering Algorithm -- Image Copy-Move Forgery Detection using Deep Convolutional Neural Networks. | |
| 520 | _aThis book presents a detailed study of key points and block-based copy-move forgery detection techniques with a critical discussion about their pros and cons. It also highlights the directions for further development in image forgery detection. The book includes various publicly available standard image copy-move forgery datasets that are experimentally analyzed and presented with complete descriptions. Five different image copy-move forgery detection techniques are implemented to overcome the limitations of existing copy-move forgery detection techniques. The key focus of work is to reduce the computational time without adversely affecting the efficiency of these techniques. In addition, these techniques are also robust to geometric transformation attacks like rotation, scaling, or both. | ||
| 650 | 0 | _aSignal processing. | |
| 650 | 0 | _aComputer vision. | |
| 650 | 0 | _aComputational intelligence. | |
| 650 | 1 | 4 | _aDigital and Analog Signal Processing. |
| 650 | 2 | 4 | _aComputer Vision. |
| 650 | 2 | 4 | _aComputational Intelligence. |
| 653 | 0 | _aImage processing -- Digital techniques | |
| 653 | 0 | _aDigital images -- Forgeries | |
| 653 | 0 | _aDigital forensic science | |
| 700 | 1 |
_aDas, Pradip K. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 830 | 0 |
_aStudies in Computational Intelligence, _x1860-9503 ; _v1017 |
|
| 856 | 4 | 0 |
_uhttps://doi.org/10.1007/978-981-16-9041-9 _3Springer eBooks _zOnline access link to the resource |
| 942 |
_2lcc _cEBK |
||