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correlation coefficient Was it your camera that took this image? ◮ cameras are identified by measuring the similarity of the noise residual of a questionable image and all known reference noise pattern ⊲ correlation coefficient or (more preferably) peak to correlation energy 0.3 0.2 0.1 0 reference pattern: S70 Canon S70 Canon S45 Epson 1240U Ixus IIs 1 image index 350 Closed camera set problem: ◮ choose the camera with the highest similarity Open camera set problem: ◮ no decision if maximum similarity is below a certain threshold ◮ large-scale test with more than 10 6 images from ≈ 7000 individual cameras: 97.6 % correct identification at a false acceptance rate of < 3 × 10 −5 [Goljan et al., 2009] Gloe & Kirchner Digital Image Forensics slide 28
Image source identfication: Summary ◮ discussed methods are only the tip of the iceberg ◮ further approaches include: ⊲ sensor dust [Dirik et al., 2007] ⊲ JPEG quantization tables [Farid, 2008] ⊲ chromatic abberation [Van et al., 2007] ⊲ demosaicing artifacts [Swaminathan et al, 2007] ⊲ and many more . . . ◮ current literature concludes that sensor noise is the method of choice whenever we have access to the questionable device (or a number of reference images) ⊲ extensions to flatbed scanners [Gloe et al., 2007] ⊲ robust against JPEG compression; may even survive printing and re-scanning ⊲ but: computational issues with de-synchronization (cropping, rotation) ◮ device model identification beneficial whenever we don’t have direct access to the questionable device ⊲ but: does it scale with the number of examined models? Gloe & Kirchner Digital Image Forensics slide 29
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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
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 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: correlation coefficient Was it your
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
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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
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Page 127 and 128: 3Digital image forensics requires i
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Page 131 and 132: How to capture 14 000 images . . .
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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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