| 000 | 05760naaaa2201369uu 4500 | ||
|---|---|---|---|
| 003 | BUT | ||
| 005 | 20230404104536.0 | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20220224s2022 xx |||||o ||| eng|| d | ||
| 020 | _a9783036528076 | ||
| 020 | _a9783036528069 | ||
| 040 |
_aoapen _coapen |
||
| 041 | 0 | _aeng | |
| 080 | _a004.056.5 | ||
| 100 | 1 |
_aAmerini, Irene _4edt |
|
| 245 | 1 | 0 | _aImage and Video Forensics |
| 260 |
_aBasel _bMDPI - Multidisciplinary Digital Publishing Institute _c2022 |
||
| 300 | _a1 electronic resource (424 p.) | ||
| 506 | 0 |
_aOpen Access _2star _fUnrestricted online access |
|
| 520 | _aNowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity. | ||
| 540 |
_aCreative Commons _fhttps://creativecommons.org/licenses/by/4.0/ _2cc |
||
| 546 | _aEnglish | ||
| 650 | 0 |
_aКриптография, безопасность _92233 |
|
| 653 | _aface morphing | ||
| 653 | _aforensics detection | ||
| 653 | _aface landmarks | ||
| 653 | _aautomatic border control | ||
| 653 | _abiometrics | ||
| 653 | _afacial recognition | ||
| 653 | _afacial anti-spoofing | ||
| 653 | _afacial Presentation Attack Detection (PAD) | ||
| 653 | _aRGB camera-based anti-spoofing methods | ||
| 653 | _adeep learning | ||
| 653 | _asurvey | ||
| 653 | _acomputer vision | ||
| 653 | _apattern recognition | ||
| 653 | _aPRNU | ||
| 653 | _aphoto response non-uniformity | ||
| 653 | _asource camera identification | ||
| 653 | _avideos | ||
| 653 | _acompression | ||
| 653 | _asnapchat | ||
| 653 | _aresolution | ||
| 653 | _acamera fingerprint | ||
| 653 | _asmartphone identification | ||
| 653 | _auser profile linking | ||
| 653 | _adigital investigations | ||
| 653 | _asocial network | ||
| 653 | _aclassification | ||
| 653 | _ainter-frame forgery | ||
| 653 | _adigital forensics | ||
| 653 | _acorrelation | ||
| 653 | _aSVD | ||
| 653 | _aHarris | ||
| 653 | _aGLCM | ||
| 653 | _aTensor | ||
| 653 | _avideo forensic | ||
| 653 | _adigital image forensics | ||
| 653 | _asource identification | ||
| 653 | _aGAN-generated image detection | ||
| 653 | _acopy-move forgery detection | ||
| 653 | _afake image | ||
| 653 | _atransfer learning | ||
| 653 | _aVGG | ||
| 653 | _aimage forensics | ||
| 653 | _afake image detection | ||
| 653 | _aneural network | ||
| 653 | _aDeepfake | ||
| 653 | _aanomaly detection | ||
| 653 | _aUAV videos | ||
| 653 | _adeep one-class | ||
| 653 | _acybersecurity | ||
| 653 | _amultimedia content manipulation | ||
| 653 | _adeepfake | ||
| 653 | _aconvolutional neural networks | ||
| 653 | _asupport vector machines | ||
| 653 | _adiscrete fourier transform | ||
| 653 | _aDeepFake detection | ||
| 653 | _ahand-crafted features | ||
| 653 | _aforensic process model | ||
| 653 | _aplausibility of decisions | ||
| 653 | _aforensic evidence evaluation | ||
| 653 | _avideo source attribution | ||
| 653 | _alikelihood ratio | ||
| 653 | _aperformance | ||
| 653 | _ablind estimation | ||
| 653 | _aforged image detection | ||
| 653 | _aheatmap | ||
| 653 | _aJPEG | ||
| 653 | _anoise level function | ||
| 653 | _adeepfake detection | ||
| 653 | _aGenerative Adversarial Networks | ||
| 653 | _amultimedia forensics | ||
| 653 | _acamera model identification | ||
| 653 | _avideo forensics | ||
| 653 | _aaudio forensics | ||
| 653 | _amedia forensics | ||
| 653 | _asocial media platform identification | ||
| 653 | _adeepfakes | ||
| 653 | _afacial manipulations | ||
| 653 | _asocial networks | ||
| 653 | _aestimation by rotational invariant techniques (ESPRIT) | ||
| 653 | _ashort-time Fourier transform (STFT) | ||
| 653 | _amultiple signal classification (MUSIC) | ||
| 653 | _asimple linear iterative clustering (SLIC) | ||
| 653 | _an/a | ||
| 700 | 1 |
_aBaldini, Gianmarco _4edt |
|
| 700 | 1 |
_aLeotta, Francesco _4edt |
|
| 700 | 1 |
_aAmerini, Irene _4oth |
|
| 700 | 1 |
_aBaldini, Gianmarco _4oth |
|
| 700 | 1 |
_aLeotta, Francesco _4oth |
|
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://mdpi.com/books/pdfview/book/4812 _70 _zDownload |
| 856 | 4 | 0 |
_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/78721 _70 _zDescription |
| 909 |
_c4 _dDarya Shvetsova |
||
| 942 |
_2udc _cEE |
||
| 999 |
_c6331 _d6331 |
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