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Models: yet another dimensionality reduction ◮ models make projection of reality to discrete symbols tractable with formal methods ◮ typical models in image forensics: ⊲ sensor noise follows a Gaussian distribution ⊲ connected regions of identical pixel values are unlikely to occur in original images projection to a 1-dimensional variable ◮ models of reality are yet another dimensionality reduction ◮ quality of forensic methods depends on the quality of the employed model! Gloe & Kirchner Digital Image Forensics slide 16 p
Models: yet another dimensionality reduction ◮ models make projection of reality to discrete symbols tractable with formal methods ◮ typical models in image forensics: ⊲ sensor noise follows a Gaussian distribution ⊲ connected regions of identical pixel values are unlikely to occur in original images projection to a 1-dimensional variable ◮ models of reality are yet another dimensionality reduction ◮ quality of forensic methods depends on the quality of the employed model! Gloe & Kirchner Digital Image Forensics slide 16 p
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I know what you have done in your l
Page 3 and 4: This is actually not from a horror
Page 5 and 6: This is actually not from a horror
Page 7 and 8: . . . yet it is as scary, at least.
Page 9 and 10: . . . yet it is as scary, at least.
Page 11 and 12: Authenticity of images in the digit
Page 13 and 14: Digital image forensics: a first gl
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: 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
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Resampling detection scheme example
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Resampling detection scheme ωt ′
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Resampling detection scheme ωt ′
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It’s not only the biggest potato
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Color filter array interpolation
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Example application of CFA artifact
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Example application of CFA artifact
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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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