5,347 research outputs found
CoMoFoD #x2014; New database for copy-move forgery detection
Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofodMinistry of Science, Education and Sport, China; project numbers: 036-0361630-1635 and 036-0361630-164
Analysis of adversarial attacks against CNN-based image forgery detectors
With the ubiquitous diffusion of social networks, images are becoming a
dominant and powerful communication channel. Not surprisingly, they are also
increasingly subject to manipulations aimed at distorting information and
spreading fake news. In recent years, the scientific community has devoted
major efforts to contrast this menace, and many image forgery detectors have
been proposed. Currently, due to the success of deep learning in many
multimedia processing tasks, there is high interest towards CNN-based
detectors, and early results are already very promising. Recent studies in
computer vision, however, have shown CNNs to be highly vulnerable to
adversarial attacks, small perturbations of the input data which drive the
network towards erroneous classification. In this paper we analyze the
vulnerability of CNN-based image forensics methods to adversarial attacks,
considering several detectors and several types of attack, and testing
performance on a wide range of common manipulations, both easily and hardly
detectable
Quality criteria benchmark for hyperspectral imagery
Hyperspectral data appear to be of a growing interest
over the past few years. However, applications for hyperspectral
data are still in their infancy as handling the significant size of
the data presents a challenge for the user community. Efficient
compression techniques are required, and lossy compression,
specifically, will have a role to play, provided its impact on remote
sensing applications remains insignificant. To assess the data
quality, suitable distortion measures relevant to end-user applications
are required. Quality criteria are also of a major interest
for the conception and development of new sensors to define their
requirements and specifications. This paper proposes a method to
evaluate quality criteria in the context of hyperspectral images.
The purpose is to provide quality criteria relevant to the impact
of degradations on several classification applications. Different
quality criteria are considered. Some are traditionnally used in
image and video coding and are adapted here to hyperspectral
images. Others are specific to hyperspectral data.We also propose
the adaptation of two advanced criteria in the presence of different
simulated degradations on AVIRIS hyperspectral images. Finally,
five criteria are selected to give an accurate representation of the
nature and the level of the degradation affecting hyperspectral
data
Contextual biometric watermarking of fingerprint images
This research presents contextual digital watermarking techniques using face and demographic text data as multiple watermarks for protecting the evidentiary integrity of fingerprint image. The proposed techniques embed the watermarks into selected regions of fingerprint image in MDCT and DWT domains. A general image watermarking algorithm is developed to investigate the application of MDCT in the elimination of blocking artifacts. The application of MDCT has improved the performance of the watermarking technique compared to DCT. Experimental results show that modifications to fingerprint image are visually imperceptible and maintain the minutiae detail. The integrity of the fingerprint image is verified through high matching score obtained from the AFIS system. There is also a high degree of correlation between the embedded and extracted watermarks. The degree of similarity is computed using pixel-based metrics and human visual system metrics. It is useful for personal identification and establishing digital chain of custody. The results also show that the proposed watermarking technique is resilient to common image modifications that occur during electronic fingerprint transmission
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