Detection of copy{move forgeries is one of the most actively researched topics in image forensics. It has been shown that so-called block-based methods give the highest pixel-wise accuracy for detecting copy{move forgeries. However, matching of block-based features can be computationally extremely demanding. Hence, the current predominant line of thought is that block-based algorithms are too slow to be applicable in practice. In this paper, we revisit the matching stage of block-based copy{move forgery detection methods. We propose an efficient approach for finding duplicate patterns of a given size in integer-valued input data. By design, we focus on the spatial relation of potentially duplicated elements. This allows us to locate copy{move forgeries via bit-wise operations, without expensive block comparisons in the feature space. Experimental investigation of different matching strategies shows that the proposed method has its benefits. However, on a broader scale, our experiments demonstrate that the performance of matching by lexicographic sorting might have been underestimated in previous work, despite its remarkable speed benefit on large images. In fact, in a practical setting, where accuracy and computational efficiency have to be balanced, lexicographic sorting may be considered the method of choice.
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.
This paper proposes an efficient method to determine the concrete configuration of the color filter array (CFA)
from demosaiced images. This is useful to decrease the degrees of freedom when checking for the existence or
consistency of CFA artifacts in typical digital camera images. We see applications in a wide range of multimedia
security scenarios whenever inter-pixel correlation plays an important role. Our method is based on a CFA
synthesis procedure that finds the most likely raw sensor output for a given full-color image. We present
approximate solutions that require only one linear filtering operation per image. The effectiveness of our method
is demonstrated by experimental results from a large database of images.
In digital image forensics, it is generally accepted that intentional manipulations of the image content are
most critical and hence numerous forensic methods focus on the detection of such 'malicious' post-processing.
However, it is also beneficial to know as much as possible about the general processing history of an image,
including content-preserving operations, since they can affect the reliability of forensic methods in various ways.
In this paper, we present a simple yet effective technique to detect median filtering in digital images-a widely
used denoising and smoothing operator. As a great variety of forensic methods relies on some kind of a linearity
assumption, a detection of non-linear median filtering is of particular interest. The effectiveness of our method
is backed with experimental evidence on a large image database.
We propose a method to synthetically create or restore typical color filter array (CFA) pattern in digital images.
This can be useful, inter alia, to conceal traces of manipulation from forensic techniques that analyze the CFA
structure of images. For continuous signals, our solution maintains optimal image quality, using a quadratic
cost function; and it can be computed efficiently. Our general approach allows to derive even more efficient
approximate solutions that achieve linear complexity in the number of pixels. The effectiveness of the CFA
synthesis as tamper-hiding technique and its superior image quality is backed with experimental evidence on
large image sets and against state-of-the-art forensic techniques. This exposition is confined to the most relevant
'Bayer'-grid, but the method can be generalized to other layouts as well.
In Ref. 15, we took a critical view on the reliability of forensic techniques as tools to generate evidence of
authenticity for digital images and presented targeted attacks against the state-of-the-art resampling detector by
Popescu and Farid. We demonstrated that a correct detection of manipulations can be impeded by resampling
with geometric distortion. However, we constrained our experiments to global image transformations. In a more
realistic scenario, most forgeries will make use of local resampling operations, e.g., when pasting a beforehand
scaled or rotated object. In this paper, we investigate the detectability of local resampling without and with
geometric distortion and study the influence of the size both of the tampered and the analyzed image region.
Although the detector might fail to reveal the characteristic periodic resampling artifacts, a forensic investigator
can benefit from the generally increased correlation in resampled image regions. We present an adapted targeted
attack, which allows for an increased degree of undetectability in the case of local resampling.
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