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Fundamentals of digital image forensics synthetic 3D scene real scene lightning, shadows analog 2D document surface characteristics texture digitalization device characteristics digital image file format manipulation processing artifacts output device characteristics re-digitalization analog image Gloe & Kirchner Digital Image Forensics slide 18
Fundamentals of digital image forensics synthetic 3D scene real scene lightning, shadows analog 2D document surface characteristics texture digitalization device characteristics rendering digital image file format manipulation processing artifacts output device characteristics re-digitalization analog image Gloe & Kirchner Digital Image Forensics slide 18
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I know what you have done in your l
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This is actually not from a horror
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This is actually not from a horror
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. . . yet it is as scary, at least.
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. . . yet it is as scary, at least.
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Authenticity of images in the digit
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Digital image forensics: a first gl
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Page 19 and 20: Two introductory examples ◮ digit
Page 25 and 26: 1 After all, it’s all about bits
Page 27 and 28: By the way, what is computer forens
Page 29 and 30: By the way, what is computer forens
Page 31 and 32: Digital forensics: proposed ontolog
Page 33 and 34: WARNING! The following slides inten
Page 35 and 36: Computer forensics = Image forensic
Page 37 and 38: Computer forensics = Image forensic
Page 39 and 40: Computer forensics: a closer look p
Page 41 and 42: suspicious traces? Computer forensi
Page 43 and 44: suspicious traces? Computer forensi
Page 45 and 46: Image forensics: a closer look orig
Page 47 and 48: Image forensics: a closer look orig
Page 49 and 50: Sensors: a source of uncertainty
Page 51 and 52: Sensors: a source of uncertainty
Page 53 and 54: Models: yet another dimensionality
Page 55 and 56: Models: yet another dimensionality
Page 57 and 58: Fundamentals of digital image foren
Page 69: Fundamentals of digital image foren
Page 75 and 76: Image source identification goal: d
Page 77 and 78: Intra- and inter-model similarities
Page 79 and 80: Camera model identification ◮ mos
Page 81 and 82: Some results on camera model identi
Page 83 and 84: Camera model identification: overal
Page 85 and 86: Camera device identification ◮ mo
Page 87 and 88: Camera device identification ◮ mo
Page 89 and 90: A typical noise residual Wavelet de
Page 91 and 92: correlation coefficient Was it your
Page 93 and 94: Image source identfication: Summary
Page 95 and 96: Need a break? Ooops. . . submission
Page 97 and 98: Image manipulation detection Digita
Page 103 and 104: Resampling detection ◮ image mani
Page 105 and 106: Resampling detection scheme example
Page 107 and 108: Resampling detection scheme ωt ′
Page 109 and 110: Resampling detection scheme ωt ′
Page 111 and 112: It’s not only the biggest potato
Page 113 and 114: Color filter array interpolation
Page 115 and 116: Example application of CFA artifact
Page 117 and 118: Example application of CFA artifact
Page 119 and 120: Double JPEG compression Main idea /
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Double JPEG compression artifacts Q
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Church of our Ladies, Dresden A typ
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. . . and a practical application J
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3Digital image forensics requires i
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How does your algorithm know that t
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How to capture 14 000 images . . .
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min / max temperature [ ◦ C] 8 4
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DOCMA Award Background ◮ creating
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DOCMA Award: some examples Original
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4Now that we have the theory behind
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Digital forensics: proposed ontolog
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Counter-forensics: computer forensi
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Counter-forensics: image forensics
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Attacks: practical considerations W
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fin Concluding remarks
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Multimedia forensics: a growing fie
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A broader view 2 2 7 7 ICSI 2010 2A
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Computer forensics in a broader sen
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Computer forensics in a broader sen
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Image sources ⊲ Photoshop logo (t
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