A hybrid model for digital camera source identification
Digital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the images. In conjunction with image processing techniques and data...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2904 |
|---|---|
| container_issue | |
| container_start_page | 2901 |
| container_title | |
| container_volume | |
| creator | Min-Jen Tsai Cheng-Sheng Wang Jung Liu |
| description | Digital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the images. In conjunction with image processing techniques and data exploration methods, it calculates the characteristic values of the images taken by different cameras. Training and categorization are conducted through the support vector machines for identifying the camera source of the images. Based on the fact that the internal imaging algorithms of a camera are different from one manufacturer to another, this method computes the hidden characteristics of the images. The result of experiments shows the proposed approach can obtains 94.95% identification rate when images are taken from 20 different cameras. |
| doi_str_mv | 10.1109/ICIP.2009.5413414 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_5413414</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5413414</ieee_id><sourcerecordid>5413414</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-26f6e9b392807520ce34811e5318ae348e19bf9ba655464e972ac9c7b098602e3</originalsourceid><addsrcrecordid>eNpVkM1Kw0AUhcc_MNY-gLiZF0i8d_5nWYLVQEEXui6TyY2OpI1M4qJvr2I3rs6BDw58h7EbhAoR_F1TN8-VAPCVVigVqhO29NahEkppozWcskJIh6XTyp_9Y9KcswK1EKVyDi7Z1TR9AAhAiQWzK_5-aHPq-G7saOD9mHmX3tIcBh7DjnLg0_iVI_HU0X5OfYphTuP-ml30YZhoecwFe13fv9SP5ebpoalXmzKh1XMpTG_It9ILB1YLiCSVQyQt0YXfTujb3rfhR0EZRd6KEH20LXhnQJBcsNu_3URE28-cdiEftscP5DdypEmq</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A hybrid model for digital camera source identification</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Min-Jen Tsai ; Cheng-Sheng Wang ; Jung Liu</creator><creatorcontrib>Min-Jen Tsai ; Cheng-Sheng Wang ; Jung Liu</creatorcontrib><description>Digital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the images. In conjunction with image processing techniques and data exploration methods, it calculates the characteristic values of the images taken by different cameras. Training and categorization are conducted through the support vector machines for identifying the camera source of the images. Based on the fact that the internal imaging algorithms of a camera are different from one manufacturer to another, this method computes the hidden characteristics of the images. The result of experiments shows the proposed approach can obtains 94.95% identification rate when images are taken from 20 different cameras.</description><identifier>ISSN: 1522-4880</identifier><identifier>ISBN: 9781424456536</identifier><identifier>ISBN: 1424456533</identifier><identifier>EISSN: 2381-8549</identifier><identifier>EISBN: 9781424456550</identifier><identifier>EISBN: 9781424456543</identifier><identifier>EISBN: 142445655X</identifier><identifier>EISBN: 1424456541</identifier><identifier>DOI: 10.1109/ICIP.2009.5413414</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biosensors ; Digital cameras ; Digital forensics ; Fingerprint recognition ; Hardware ; Image quality ; Image quality metrics ; Manufacturing ; Photo-response non-uniformity noise ; Sensor phenomena and characterization ; Support vector machines ; Watermarking</subject><ispartof>2009 16th IEEE International Conference on Image Processing (ICIP), 2009, p.2901-2904</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5413414$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27929,54924</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5413414$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Min-Jen Tsai</creatorcontrib><creatorcontrib>Cheng-Sheng Wang</creatorcontrib><creatorcontrib>Jung Liu</creatorcontrib><title>A hybrid model for digital camera source identification</title><title>2009 16th IEEE International Conference on Image Processing (ICIP)</title><addtitle>ICIP</addtitle><description>Digital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the images. In conjunction with image processing techniques and data exploration methods, it calculates the characteristic values of the images taken by different cameras. Training and categorization are conducted through the support vector machines for identifying the camera source of the images. Based on the fact that the internal imaging algorithms of a camera are different from one manufacturer to another, this method computes the hidden characteristics of the images. The result of experiments shows the proposed approach can obtains 94.95% identification rate when images are taken from 20 different cameras.</description><subject>Biosensors</subject><subject>Digital cameras</subject><subject>Digital forensics</subject><subject>Fingerprint recognition</subject><subject>Hardware</subject><subject>Image quality</subject><subject>Image quality metrics</subject><subject>Manufacturing</subject><subject>Photo-response non-uniformity noise</subject><subject>Sensor phenomena and characterization</subject><subject>Support vector machines</subject><subject>Watermarking</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9781424456536</isbn><isbn>1424456533</isbn><isbn>9781424456550</isbn><isbn>9781424456543</isbn><isbn>142445655X</isbn><isbn>1424456541</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2009</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkM1Kw0AUhcc_MNY-gLiZF0i8d_5nWYLVQEEXui6TyY2OpI1M4qJvr2I3rs6BDw58h7EbhAoR_F1TN8-VAPCVVigVqhO29NahEkppozWcskJIh6XTyp_9Y9KcswK1EKVyDi7Z1TR9AAhAiQWzK_5-aHPq-G7saOD9mHmX3tIcBh7DjnLg0_iVI_HU0X5OfYphTuP-ml30YZhoecwFe13fv9SP5ebpoalXmzKh1XMpTG_It9ILB1YLiCSVQyQt0YXfTujb3rfhR0EZRd6KEH20LXhnQJBcsNu_3URE28-cdiEftscP5DdypEmq</recordid><startdate>200911</startdate><enddate>200911</enddate><creator>Min-Jen Tsai</creator><creator>Cheng-Sheng Wang</creator><creator>Jung Liu</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200911</creationdate><title>A hybrid model for digital camera source identification</title><author>Min-Jen Tsai ; Cheng-Sheng Wang ; Jung Liu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-26f6e9b392807520ce34811e5318ae348e19bf9ba655464e972ac9c7b098602e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Biosensors</topic><topic>Digital cameras</topic><topic>Digital forensics</topic><topic>Fingerprint recognition</topic><topic>Hardware</topic><topic>Image quality</topic><topic>Image quality metrics</topic><topic>Manufacturing</topic><topic>Photo-response non-uniformity noise</topic><topic>Sensor phenomena and characterization</topic><topic>Support vector machines</topic><topic>Watermarking</topic><toplevel>online_resources</toplevel><creatorcontrib>Min-Jen Tsai</creatorcontrib><creatorcontrib>Cheng-Sheng Wang</creatorcontrib><creatorcontrib>Jung Liu</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Min-Jen Tsai</au><au>Cheng-Sheng Wang</au><au>Jung Liu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A hybrid model for digital camera source identification</atitle><btitle>2009 16th IEEE International Conference on Image Processing (ICIP)</btitle><stitle>ICIP</stitle><date>2009-11</date><risdate>2009</risdate><spage>2901</spage><epage>2904</epage><pages>2901-2904</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9781424456536</isbn><isbn>1424456533</isbn><eisbn>9781424456550</eisbn><eisbn>9781424456543</eisbn><eisbn>142445655X</eisbn><eisbn>1424456541</eisbn><abstract>Digital forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This study has focused on analyzing the relationship between digital cameras and the images. In conjunction with image processing techniques and data exploration methods, it calculates the characteristic values of the images taken by different cameras. Training and categorization are conducted through the support vector machines for identifying the camera source of the images. Based on the fact that the internal imaging algorithms of a camera are different from one manufacturer to another, this method computes the hidden characteristics of the images. The result of experiments shows the proposed approach can obtains 94.95% identification rate when images are taken from 20 different cameras.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2009.5413414</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1522-4880 |
| ispartof | 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, p.2901-2904 |
| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_5413414 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biosensors Digital cameras Digital forensics Fingerprint recognition Hardware Image quality Image quality metrics Manufacturing Photo-response non-uniformity noise Sensor phenomena and characterization Support vector machines Watermarking |
| title | A hybrid model for digital camera source identification |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T13%3A32%3A44IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20hybrid%20model%20for%20digital%20camera%20source%20identification&rft.btitle=2009%2016th%20IEEE%20International%20Conference%20on%20Image%20Processing%20(ICIP)&rft.au=Min-Jen%20Tsai&rft.date=2009-11&rft.spage=2901&rft.epage=2904&rft.pages=2901-2904&rft.issn=1522-4880&rft.eissn=2381-8549&rft.isbn=9781424456536&rft.isbn_list=1424456533&rft_id=info:doi/10.1109/ICIP.2009.5413414&rft_dat=%3Cieee_6IE%3E5413414%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781424456550&rft.eisbn_list=9781424456543&rft.eisbn_list=142445655X&rft.eisbn_list=1424456541&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=5413414&rfr_iscdi=true |