We describe a photo forensic technique based on detecting inconsistencies in lighting. This technique explicitly measures the 3-D lighting properties for individual people, objects, or surfaces in a single image. We show that with minimal training, an analyst can accurately specify 3-D shape in a single image from which 3-D lighting can be automatically estimated. A perturbation analysis on the estimated lighting is performed to yield a probabilistic measure of the location of the illuminating light. Inconsistencies in lighting within an image evidence photo tampering.
We describe a new concept for making photo tampering more difficult and time consuming, and for a given amount of time and effort, more amenable to detection. We record the camera preview and camera motion in the moments just prior to image capture. This information is packaged along with the full resolution image. To avoid detection, any subsequent manipulation of the image would have to be propagated to be consistent with this data - a decidedly difficult undertaking.
We describe how to exploit the formation and storage of an embedded image thumbnail for image authentication.
The creation of a thumbnail is modeled with a series of filtering operations, contrast adjustment, and compression.
We automatically estimate these model parameters and show that these parameters differ significantly between
camera manufacturers and photo-editing software. We also describe how this signature can be combined with
encoding information from the underlying full resolution image to further refine the signature's distinctiveness.
While historically we may have been overly trusting of photographs, in recent years there has been a backlash
of sorts and the authenticity of photographs is now routinely questioned. Because these judgments are often
made by eye, we wondered how reliable the human visual system is in detecting discrepancies that might arise
from photo tampering. We show that the visual system is remarkably inept at detecting simple geometric
inconsistencies in shadows, reflections, and perspective distortions. We also describe computational methods
that can be applied to detect the inconsistencies that seem to elude the human visual system.
Digital audio provides a suitable cover for high-throughput
steganography. At 16 bits per sample and sampled at a rate of 44,100
Hz, digital audio has the bit-rate to support large messages. In
addition, audio is often transient and unpredictable, facilitating the hiding of messages. Using an approach similar to our universal image steganalysis, we show that hidden messages alter the underlying
statistics of audio signals. Our statistical model begins by building
a linear basis that captures certain statistical properties of audio
signals. A low-dimensional statistical feature vector is extracted
from this basis representation and used by a non-linear support vector machine for classification. We show the efficacy of this approach on LSB embedding and Hide4PGP. While no explicit assumptions about the content of the audio are made, our technique has been developed and tested on high-quality recorded speech.
Steganographic messages can be embedded into digital images in ways
that are imperceptible to the human eye. These messages, however,
alter the underlying statistics of an image. We previously built
statistical models using first-and higher-order wavelet statistics,
and employed a non-linear support vector machines (SVM) to detect
steganographic messages. In this paper we extend these results to
exploit color statistics, and show how a one-class SVM greatly
simplifies the training stage of the classifier.
Microscope-based image-guided neurosurgery can be divided into three steps: calibration of the microscope optics; registration of the pre-operative images to the operating space; and tracking of the patient and microscope over time. Critical to this overall system is the temporal retention of accurate camera calibration. Classic calibration algorithms are routinely employed to find both intrinsic and extrinsic camera parameters. The accuracy of this calibration, however, is quickly compromised due to the complexity of the operating room, the long duration of a surgical procedure, and the inaccuracies in the tracking system. To compensate for the changing conditions, we have developed an adaptive procedure which responds to accruing registration error. The approach utilizes miniature fiducial markers implanted on the bony rim of the craniotomy site, which remain in the field of view of the operating microscope. A simple error function that enforces the registration of the known fiducial markers is used to update the extrinsic camera parameters. The error function is minimized using a gradient descent. This correction procedure reduces RMS registration errors for cortical features on the surface of the brain by an average of 72%, or 1.5 mm. These errors were reduced to less than 0.6 mm after each correction during the entire surgical procedure.
We have applied techniques from differential motion estimation in the context of automatic registration of medical images. This method uses optical-flow and Fourier technique for local/global registration. A six parameter affine model is used to estimate shear, rotation, scale and translation. We show the efficacy of this method with images of similar and different contrasts.
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