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Resampling detection scheme ωt ′ − ω ωt ′ example: linear upsampling (×2) ωt ′ + ω t − 2 t − 1 t t + 1 t + 2 ◮ predictor residue: e(ωt ′ ) = s(ωt ′ ) − KX αks(ωt ′ + ωk) (α0 := 0) k=−K ◮ large absolute prediction errors indicate minor degree of linear dependence Gloe & Kirchner Digital Image Forensics slide 33
Resampling detection scheme ωt ′ − ω ωt ′ example: linear upsampling (×2) ωt ′ + ω t − 2 t − 1 t t + 1 t + 2 ◮ predictor residue: e(ωt ′ ) = s(ωt ′ ) − KX αks(ωt ′ + ωk) (α0 := 0) k=−K ◮ large absolute prediction errors indicate minor degree of linear dependence ◮ optimal weights in the example: α = (0.5, 0, 0.5) ⊲ every second sample defaults to 0 Gloe & Kirchner Digital Image Forensics slide 33
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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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Two introductory examples ◮ digit
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1 After all, it’s all about bits
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By the way, what is computer forens
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By the way, what is computer forens
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Digital forensics: proposed ontolog
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WARNING! The following slides inten
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Computer forensics = Image forensic
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Computer forensics = Image forensic
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Computer forensics: a closer look p
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suspicious traces? Computer forensi
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suspicious traces? Computer forensi
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Image forensics: a closer look orig
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Image forensics: a closer look orig
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Sensors: a source of uncertainty
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Sensors: a source of uncertainty
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Models: yet another dimensionality
Page 55 and 56: Models: yet another dimensionality
Page 57 and 58: 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: Resampling detection scheme example
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 /
Page 121 and 122: Double JPEG compression artifacts Q
Page 123 and 124: Church of our Ladies, Dresden A typ
Page 125 and 126: . . . and a practical application J
Page 127 and 128: 3Digital image forensics requires i
Page 129 and 130: How does your algorithm know that t
Page 131 and 132: How to capture 14 000 images . . .
Page 133: min / max temperature [ ◦ C] 8 4
Page 136 and 137: DOCMA Award Background ◮ creating
Page 138 and 139: DOCMA Award: some examples Original
Page 144 and 145: 4Now that we have the theory behind
Page 146 and 147: Digital forensics: proposed ontolog
Page 148 and 149: Counter-forensics: computer forensi
Page 154 and 155: 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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