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Intra- and inter-model similarities ◮ device identification – examined characteristic is unique for each device ⊲ CCD / CMOS sensor noise [Lukáˇs et al., 2005] ◮ model identification – examined characteristic is similar for all devices of one particular model and differs between different models inter-model intra-model similarity intra-model similarity similarity low for device identification high for model identification low similarity low for device identification high for model identification Gloe & Kirchner Digital Image Forensics slide 20
Camera model identification ◮ most probably, no single feature can distinguish between different camera models ⊲ find a set of features with desirable properties Black box model [Kharrazi et al., 2004; Gloe et al., 2009] ◮ overall 34 features to characterize images from the same camera model ⊲ 12 color features to capture particularities of for instance white balancing ⊲ 9 wavelet domain noise features ⊲ 13 additional ‘image quality metrics’ for a general purpose description scene lens image quality metrics filter R G G B sensor image quality metrics, wavelet statistics color interpolation post processing color features color features digital image Gloe & Kirchner Digital Image Forensics slide 21
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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
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 75 and 76: Image source identification goal: d
Page 77: Intra- and inter-model similarities
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 /
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
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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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