A Novel Algorithm of Image Splicing Detection
Recent advances in computer technology have made digital image tampering more and more common. In this paper we present an approach to passive detection of image splicing. The image splicing detection problem can be regarded as a two-class classification problem under the pattern recognition framewo...
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 | 1930 |
|---|---|
| container_issue | |
| container_start_page | 1927 |
| container_title | |
| container_volume | |
| creator | Zhu Kaizhen Zhen Zhang |
| description | Recent advances in computer technology have made digital image tampering more and more common. In this paper we present an approach to passive detection of image splicing. The image splicing detection problem can be regarded as a two-class classification problem under the pattern recognition framework. The features are extracted by image quality metrics (IQMs) and moments of characteristic functions of wavelet subbands. To evaluate the performance of our proposed model, we further present a concrete implementation of this model. Our experimental works have demonstrated that this splicing detection scheme performs effectively and have indicated that the proposed approach possesses promising capability in splicing detection. |
| doi_str_mv | 10.1109/ICICEE.2012.512 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6322803</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6322803</ieee_id><sourcerecordid>6322803</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-37d92c12afa455773f03dd3f853ca522ccb1b614619464fc5d7b4f13f2070e623</originalsourceid><addsrcrecordid>eNotjj1LxEAUAFdEUM_UFjb7BxL3vbefZYhRA4cWan0km91zJbkcSRD89x7oNNMNw9gtiAJAuPumaqq6LlAAFgrwjGXOWGG0U9I4pHN2DVIbAqkEXLJsWb7ECQuWrLpieclfpu8w8HLYT3NaP0c-Rd6M7T7wt-OQfDrs-UNYg1_TdLhhF7EdlpD9e8M-Huv36jnfvj41VbnNExi15mR6hx6wja1UyhiKgvqeolXkW4XofQedPm2Bk1pGr3rTyQgUURgRNNKG3f11Uwhhd5zT2M4_O02IVhD9AoJ9QaU</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A Novel Algorithm of Image Splicing Detection</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Zhu Kaizhen ; Zhen Zhang</creator><creatorcontrib>Zhu Kaizhen ; Zhen Zhang</creatorcontrib><description>Recent advances in computer technology have made digital image tampering more and more common. In this paper we present an approach to passive detection of image splicing. The image splicing detection problem can be regarded as a two-class classification problem under the pattern recognition framework. The features are extracted by image quality metrics (IQMs) and moments of characteristic functions of wavelet subbands. To evaluate the performance of our proposed model, we further present a concrete implementation of this model. Our experimental works have demonstrated that this splicing detection scheme performs effectively and have indicated that the proposed approach possesses promising capability in splicing detection.</description><identifier>ISBN: 1467314501</identifier><identifier>ISBN: 9781467314503</identifier><identifier>EISBN: 9780769547923</identifier><identifier>EISBN: 0769547923</identifier><identifier>DOI: 10.1109/ICICEE.2012.512</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>Arrays ; Digital image passive-blind forensics ; Feature extraction ; Histograms ; Image quality ; image quality metrics ; image splicing detection ; Measurement ; moment based features ; Splicing ; Support Vector Machine (SVM) ; Support vector machines</subject><ispartof>2012 International Conference on Industrial Control and Electronics Engineering, 2012, p.1927-1930</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/6322803$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,777,781,786,787,2052,27906,54901</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6322803$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zhu Kaizhen</creatorcontrib><creatorcontrib>Zhen Zhang</creatorcontrib><title>A Novel Algorithm of Image Splicing Detection</title><title>2012 International Conference on Industrial Control and Electronics Engineering</title><addtitle>icicee</addtitle><description>Recent advances in computer technology have made digital image tampering more and more common. In this paper we present an approach to passive detection of image splicing. The image splicing detection problem can be regarded as a two-class classification problem under the pattern recognition framework. The features are extracted by image quality metrics (IQMs) and moments of characteristic functions of wavelet subbands. To evaluate the performance of our proposed model, we further present a concrete implementation of this model. Our experimental works have demonstrated that this splicing detection scheme performs effectively and have indicated that the proposed approach possesses promising capability in splicing detection.</description><subject>Arrays</subject><subject>Digital image passive-blind forensics</subject><subject>Feature extraction</subject><subject>Histograms</subject><subject>Image quality</subject><subject>image quality metrics</subject><subject>image splicing detection</subject><subject>Measurement</subject><subject>moment based features</subject><subject>Splicing</subject><subject>Support Vector Machine (SVM)</subject><subject>Support vector machines</subject><isbn>1467314501</isbn><isbn>9781467314503</isbn><isbn>9780769547923</isbn><isbn>0769547923</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjj1LxEAUAFdEUM_UFjb7BxL3vbefZYhRA4cWan0km91zJbkcSRD89x7oNNMNw9gtiAJAuPumaqq6LlAAFgrwjGXOWGG0U9I4pHN2DVIbAqkEXLJsWb7ECQuWrLpieclfpu8w8HLYT3NaP0c-Rd6M7T7wt-OQfDrs-UNYg1_TdLhhF7EdlpD9e8M-Huv36jnfvj41VbnNExi15mR6hx6wja1UyhiKgvqeolXkW4XofQedPm2Bk1pGr3rTyQgUURgRNNKG3f11Uwhhd5zT2M4_O02IVhD9AoJ9QaU</recordid><startdate>201208</startdate><enddate>201208</enddate><creator>Zhu Kaizhen</creator><creator>Zhen Zhang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201208</creationdate><title>A Novel Algorithm of Image Splicing Detection</title><author>Zhu Kaizhen ; Zhen Zhang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-37d92c12afa455773f03dd3f853ca522ccb1b614619464fc5d7b4f13f2070e623</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Arrays</topic><topic>Digital image passive-blind forensics</topic><topic>Feature extraction</topic><topic>Histograms</topic><topic>Image quality</topic><topic>image quality metrics</topic><topic>image splicing detection</topic><topic>Measurement</topic><topic>moment based features</topic><topic>Splicing</topic><topic>Support Vector Machine (SVM)</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Zhu Kaizhen</creatorcontrib><creatorcontrib>Zhen Zhang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhu Kaizhen</au><au>Zhen Zhang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A Novel Algorithm of Image Splicing Detection</atitle><btitle>2012 International Conference on Industrial Control and Electronics Engineering</btitle><stitle>icicee</stitle><date>2012-08</date><risdate>2012</risdate><spage>1927</spage><epage>1930</epage><pages>1927-1930</pages><isbn>1467314501</isbn><isbn>9781467314503</isbn><eisbn>9780769547923</eisbn><eisbn>0769547923</eisbn><coden>IEEPAD</coden><abstract>Recent advances in computer technology have made digital image tampering more and more common. In this paper we present an approach to passive detection of image splicing. The image splicing detection problem can be regarded as a two-class classification problem under the pattern recognition framework. The features are extracted by image quality metrics (IQMs) and moments of characteristic functions of wavelet subbands. To evaluate the performance of our proposed model, we further present a concrete implementation of this model. Our experimental works have demonstrated that this splicing detection scheme performs effectively and have indicated that the proposed approach possesses promising capability in splicing detection.</abstract><pub>IEEE</pub><doi>10.1109/ICICEE.2012.512</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 1467314501 |
| ispartof | 2012 International Conference on Industrial Control and Electronics Engineering, 2012, p.1927-1930 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_6322803 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Arrays Digital image passive-blind forensics Feature extraction Histograms Image quality image quality metrics image splicing detection Measurement moment based features Splicing Support Vector Machine (SVM) Support vector machines |
| title | A Novel Algorithm of Image Splicing Detection |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-20T08%3A19%3A38IST&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%20Novel%20Algorithm%20of%20Image%20Splicing%20Detection&rft.btitle=2012%20International%20Conference%20on%20Industrial%20Control%20and%20Electronics%20Engineering&rft.au=Zhu%20Kaizhen&rft.date=2012-08&rft.spage=1927&rft.epage=1930&rft.pages=1927-1930&rft.isbn=1467314501&rft.isbn_list=9781467314503&rft.coden=IEEPAD&rft_id=info:doi/10.1109/ICICEE.2012.512&rft_dat=%3Cieee_6IE%3E6322803%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9780769547923&rft.eisbn_list=0769547923&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6322803&rfr_iscdi=true |