confidence weighting

8/2/2019 Confidence Weighting

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Presenter/Author: Scott McCloskeyHoneywell Labs, Minneapolis, MN, USA

[email protected]

Confidence Weighting for Sensor Fingerprinting


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HONEYWELL PROPRIETARY

Outline of Talk

1. Motivation for Sensor Fingerprinting

2. Review of Chens Method

3. Independent Testing & Analysis

4. Confidence Weighting to HandlePersistent Edges

5. Experimental Results

6. Future Work


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Common Source Camera Identification Problem: Given two videos (or sets of images), can we determine whether

or not they were taken with the same camera?

Scenario: Videos of two IED events are posted to YouTube. If they were

taken with the same camera, we establish a link between the events. Applications:forensic data analysis, social network analysis

Signature Data Advantages/Disadvantages

Image/video header data Quick and easy

Easily spoofedModelLevel Identification

Lens distortions Cameras w/ interchangeable/zoom lenses

CFA interpolation Monochrome images/video

Device-Level IdentificationDead pixels, dark noise Typically corrected in-camera

Photo-response non-uniformity(PRNU) of cameras sensor

Device specific Signature space is large Difficult to correct in-camera


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Photo-Response Non-Uniformity (PRNU)

Due to material and manufacturing imperfections, eachpixel on a sensor has a slightly different (non-uniform)response to incoming light. This is most noticeable inimages of uniformly-illuminated flat fields.

Step 1: Signature Extraction Step 2: Signature Comparison

1. Separate each frame into scenecontent and noise components.

2. Average noise component is thesignature.

1. Compute cross-correlation ofinput signatures.

2. Measure sharpness of peak

3. Compare to threshold

Algorithm proposed by: M. Chen, J. Fridrich, and M. Goljan in Source Digital Camcorder Identification Using Sensor Photo-Response Non-Uniformity. Proc. of SPIE, January 2007.

Because the magnitude of this noise is related to environmental conditions(temperature) and because most scenes are not flat fields, the non-uniformity is not corrected in camera.

PRNU-based sensor fingerprints can distinguish between a large numberof devices. If we presume only that we can distinguish three levels ofresponse (normal, high, low), the number of signatures for a 1MP sensoris 3

1000000, which is practically infinite.


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Signature Extraction

Unlike other applications, where Computer Vision methods to abstractaway differences between cameras to recognize scene objects (faces,etc.), we now need to abstract away differences between scenes and

recognize camera-specific signatures.

Given an input video, we remove scene contentfrom each frame by applying a de-noising methodand subtracting that result from the original.

The maximum likelihood estimate of the PRNUsignature is:

where Ik is the raw frame, Ik is the de-noised frame, Kis the number of

frames, and Pis the signature.

Input

SceneContent

Noise

^

^


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Signature Comparison1. Compute cross-correlation of signatures at different scales.

2. Measure the magnitude of the peak using Peak-to-Secondary Ratio(PSR). This is simply the ratio of the heights of the largest and secondlargest peaks in the cross-correlation.

3. Compare the PSR to a threshold that determines whether the twovideos are said to match.

MismatchMatch

Videos from the same camera will have similar PRNU patterns, and theircross-correlation function will appear similar to a delta function. Mismatchedvideos will have dissimilar PRNU patterns, and the cross-correlation will be arandom pattern.


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Evaluating Chens Algorithm: Test Videos

Testing presented in the original paper was somewhat limited,with little analysis of the results.

In order to understand the strengths and weaknesses of thealgorithm, we test it against a suite of videos which representa wide range of potential inputs: indoor/outdoor scenes

zooming/moving/stationary camera

flat fields, highly-textured scenes

image stabilization

data from camcorders and digital still cameras with video mode

night mode (feature on camcorders) and daylight mode

When available, video data is acquired without compression. All test videos are 30f.p.s. for 40 seconds (K=1200).

Test uncompressed video, as well as XVID-compressedderivatives.



Evaluating Chens Algorithm: Results

Aoutdoor, movingB indoor, flat fieldC indoor, tripodD indoor, movingE indoor, movingF outdoor, stabilizationG indoor, movingH indoor, zoomingI indoor, moving (night mode)Jflat field (night mode)X indoor, moving

Key

True Match

True Non-match

False Match

False Non-match

Test Scenes:



Problem 1: Digital Image Stabilization

A common feature on most video cameras, imagestabilization compensates for camera motion that may

disorient or nauseate the viewer. Opticalimage stabilization uses a floating lens element to

smooth out camera motion. Not a problem.

Digitalimage stabilization uses sensors to measure cameramotion. Digitized frames are shifted to compensate.

The PRNU estimate relates to the sensitivity of sensor pixels.A pixel location in the video is assumed to correspond to thesame sensor location in each frame. The shifting of framesviolates this assumption.

We are attempting to characterize the extent to whichstabilization can be handled, in terms of the percentage offrames that are shifted.



Problem 2: Persistent Edge Content

De-noising has been long studied in image processing, and the problem iswell known to be ill-posed.

Most de-noising methods misclassify some portion of high-frequencyscene content as noise, particularly near edges.

When estimating the signature, then, the area around edges will beproblematic. If the video features stationary objects, as is the case withtripod-mounted cameras, edges appear in the extracted signature.

Edges in the signature can cause mis-classifications, particularly falsenegatives. False positives may also occur, if these spurious edges appearin similar locations in videos from different cameras.

Interview Video

Extracted Signature



Confidence Weighting

Chens method treats each pixel of each frame the same,regardless of its content. This conflicts with the intuition that

flat regions of a scene are more useful for PRNU estimation. In light of the relative difficulty inherent in noise separation

near edges, we should endeavor to avoid the inevitableerrors contributing significantly to the estimated signature.

Based on this reasoning, we propose confidence weighting

for sensor fingerprinting. Specifically, we wish to preventerroneous noise estimates near texture/edges from distortingthe estimated signature. Within frames, we weight againstregions likely to produce erroneous noise estimates.



Confidence Weighting for Persistent Edge Content

Interview Video

Extracted Signature

Confidence Map

General Idea: Analyze each frame to predict failures of the de-noising method. Use this togenerate a confidence map that weights the contribution of different scene regions to theestimated fingerprint. Low-confidence regions are not allowed to introduce spurious features tothe fingerprint.

Experiments use the confidence weight

where pis a pixel, Gis a Gaussian filter, and is the gradient operator.



Experimental Results

Old Method New Method

Aoutdoor, movingB indoor, flat fieldC indoor, tripodD indoor, movingE indoor, movingF outdoor, stabilizationG indoor, movingH indoor, zoomingI indoor, moving (night mode)

Jflat field (night mode)X indoor, moving

Key

True Match

True Non-match

False Match

False Non-match

Test Scenes:



Other Applications of Confidence Weighting

We have shown that confidence weighting can be used toimprove the quality of extracted PRNU signatures by

discriminating between regions within frames. The same framework can be expanded to includediscrimination between frames, based on their differing utilityto signature estimation. We plan to investigate two cues: signal amplification (gain) per frame. Cameras adjust to varying light

by modifying the gain, increasing it when illumination decreases.Frames with higher gain will have relatively higher levels of noise, fromwhich PRNU will be better estimated.

keyframe/interframe characterization. Most video compressionformats are heterogeneous, with certain keyframes preserved at ahigher quality. Noise estimated from such frames are likely to be more

useful for PRNU estimation.

In addition to relative discrimination, confidence measurescan be used to determine when the extracted signature issufficient, or whether more/better frames are needed.

confidence weighting

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