In this paper we address the problem of identification of pictures and videos re-captured from LCD screens. We show that they often exhibit detectable periodic patterns that are caused by regular sampling grid of LCD screen and aliasing. We develop a method capable of detecting these patterns by using the theory of cyclostationarity. The term cyclostationarity refers to a special class of signals which exhibit periodicity in their statistics. Such signals have a frequency spectrum correlated with a shifted version of itself. Experimental results quantifying the performance of the developed method are also shown.

In this paper, we analyze and analytically describe the multiplicative deterministic noise component of imaging sensors in a novel way. Specifically, we show how to use the multiplicative nature of this component to derive a method enabling its estimation. Since this noise component is unique per sensor, consequently, the derived method is applied on digital image ballistics tasks in order to pinpoint the exact device that created a specific digital photo. Moreover, we enhance the method to be resistent to optical zoom and JPEG compression.

Many methods for verifying the integrity of digital images employ various fingerprints associated with acquisition devices. Data on an acquisition device and fingerprints are extracted from an image and confronted with a reference data set that includes all possible fingerprints of the acquisition device. This allows us to draw a conclusion whether the digital image has been modified or not. Thus it is critical to have a sufficiently large, reliable, and true reference data set, otherwise critical miscalculations can arise. Reference data sets are extracted from image data sets that in turn are collected from unknown and nonguaranteed environments (mostly from the Internet). Since often software modifications leave no obvious traces in the image file (e.g., in metadata), it is not easy to recognize original images, from which fingerprints of acquisition devices can be extracted to form true reference data sets. This is the problem addressed in this paper. Given a database consisting of “unguaranteed” images, we introduce a statistical approach for assessing image originality by using the image file's header information (e.g., JPEG compression parameters). First a general framework is introduced. Then the framework is applied to several fingerprint types selected for image integrity verification.

The processing history of images plays an important role in many fields of digital image processing and computer vision. In this paper, we focus on geometrical transformations and show that images that have undergone such transformations contain hidden cyclostationary features. This makes possible employing the well-developed theory and efficient methods of cyclostationarity for blind analyzing of history of images in respect to geometrical transformations. To verify this, we also propose a cyclostationarity detection method and show how the traces of geometrical transformations in an image can be detected and the specific parameters of the transformation estimated. The method is based on the fact that a cyclostationary signal has a frequency spectrum correlated with a shifted version of itself.

In this paper, we analyze and analytically describe the specific statistical changes brought into the covariance structure of signal by the interpolation process. We show that interpolated signals and their derivatives contain specific detectable periodic properties. Based on this, we propose a blind, efficient, and automatic method capable of finding traces of resampling and interpolation. The proposed method can be very useful in many areas, especially in image security and authentication. For instance, when two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations, such as scaling, rotation, or skewing are needed. These procedures are typically based on a resampling and interpolation step. By having a method capable of detecting the traces of resampling, we can significantly reduce the successful usage of such forgeries. Among other points, the presented method is also very useful in estimation of the geometric transformations factors.

In our society digital images are a powerful and widely used communication medium. They have an important impact on our life. In recent years, due to the advent of high-performance consumer hardware and improved human computer interfaces, it has become relatively easy to create fake images. Typically, to create high quality and consistent forgeries, several types of tampering techniques are employed simultaneously. One of the most often used techniques is resampling (especially when two or more images are spliced together). Generally, resampling is based on an interpolation procedure. Typically, this procedure brings into the signal specific statistical changes. In this work we analyze and analytically describe specific periodic properties presence in interpolated signals and their derivatives. Furthermore, we propose an efficient and blind method to automatically detect the traces of geometric transformations in images and their portions. The results of the proposed method are demonstrated on several examples.