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Authenticity of images in the digital age? ◮ Where is the picture coming from? Approaches ◮ (How) Has the image been processed? ◮ cryptography or digital watermarks ⊲ marks needs to be added / embedded “actively” (high-end digital cameras) ◮ digital image forensics ⊲ does not assume any knowledge about the original image ⊲ is based on a statistical analysis of the image content N (µ, σ) Σ → min statistical analysis source? original? Gloe & Kirchner Digital Image Forensics slide 4
Digital image forensics: a first glimpse ◮ digital image forensics assess the authenticity of digital images by exploiting their inherent statistical characteristics source identification manipulation detection Gloe & Kirchner Digital Image Forensics slide 5
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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: Authenticity of images in the digit
Page 15 and 16: Digital image forensics: a first gl
Page 17 and 18: 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 and 54: Models: yet another dimensionality
Page 55 and 56: Models: yet another dimensionality
Page 57 and 58: Fundamentals of digital image foren
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Fundamentals of digital image foren
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Image source identification goal: d
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Intra- and inter-model similarities
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Camera model identification ◮ mos
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Some results on camera model identi
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Camera model identification: overal
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Camera device identification ◮ mo
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Camera device identification ◮ mo
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A typical noise residual Wavelet de
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correlation coefficient Was it your
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Image source identfication: Summary
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Need a break? Ooops. . . submission
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Image manipulation detection Digita
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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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