Slides

More documents

Recommendations

Info

Digital image forensics: a first glimpse images represent reality ⊲ empirical science ◮ digital image forensics assess the authenticity of digital images by exploiting their inherent statistical characteristics requires a large body of reference data samples class ‘legitimate’ model What means source? source identification manipulation detection device What means manipulation? ‘malicious’ Gloe & Kirchner Digital Image Forensics slide 5
Digital image forensics: a first glimpse images represent reality 1 ⊲ empirical science 2 3 ◮ digital image forensics assess the authenticity of digital images by exploiting their inherent statistical characteristics requires a large body of reference data samples class ‘legitimate’ model What means source? source identification manipulation detection device What means manipulation? ‘malicious’ Gloe & Kirchner Digital Image Forensics slide 5
Page 1 and 2: 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 15: 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
Page 67 and 68:
Fundamentals of digital image foren
Page 69 and 70:
Page 71 and 72:
Page 73 and 74:
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 99 and 100:
Page 101 and 102:
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
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 140 and 141:
Page 142 and 143:
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 150 and 151:
Page 152 and 153:
Page 154 and 155:
Counter-forensics: image forensics
Page 156 and 157:
Attacks: practical considerations W
Page 158 and 159:
fin Concluding remarks
Page 160 and 161:
Multimedia forensics: a growing fie
Page 162 and 163:
A broader view 2 2 7 7 ICSI 2010 2A
Page 164 and 165:
Computer forensics in a broader sen
Page 166 and 167:
Computer forensics in a broader sen
Page 168:
Image sources ⊲ Photoshop logo (t
show all

Slides

Create successful ePaper yourself

Delete template?

Save as template?