Over a hundred years ago, Simon Newcomb observed a surprising pattern in the distribution of the leading digits in logarithm tables: the digit 1 is significantly more likely to occur than the digit 2, which is more likely to occur than the digit 3, and so on. More than fifty years later, Frank Benford rediscovered this same pattern in more data sets such as the stock market, census data, accounting data, and more. Because fabricated data tends not to follow this same pattern, this phenomenological law has been used to detect accounting, tax, and scientific fraud. More recently, this law has been applied to detect various forms of image tampering.
Welcome to the Fourandsix blog, where you’ll find tips on image forensics techniques and commentary on issues relevant to photo tampering and the responsible use of imaging tools.
Advances in computer graphics, computing hardware, and raw artistic talent are leading to images that continually push the boundary of photo-realistic rendering. Even the most cutting edge images, however, are still limited with respect to their photo-realism.
The same MIT researchers that brought us “Seeing Behind the Camera” now bring us an amazing new video analysis tool. The tool, termed Eulerian Video Magnification, amplifies tiny modulations in a video allowing you to see things in the video that are otherwise invisible under normal viewing.
Measurements made in an image are inherently noisy. This is due to limited resolution, compression artifacts, lens distortion, etc. In some cases these errors will have only a small effect on your analysis. In other cases, these errors can effectively render your analysis useless. It is important, therefore, to take these errors into consideration.
All cameras introduce varying degrees of imperfections. While these imperfections are undesirable for the consumer, they can be highly informative in an image forensic setting. Sensor imperfections, thermal effects, compression, etc. each introduce noise in the image. This noise manifests itself as slight variations in the recorded intensities of light. If a portion of an image is altered then it is likely that the underlying noise pattern will be disrupted. Differences in the noise pattern can therefore be used to detect and localize image tampering.