1,226 research outputs found
Camera-based Image Forgery Localization using Convolutional Neural Networks
Camera fingerprints are precious tools for a number of image forensics tasks.
A well-known example is the photo response non-uniformity (PRNU) noise pattern,
a powerful device fingerprint. Here, to address the image forgery localization
problem, we rely on noiseprint, a recently proposed CNN-based camera model
fingerprint. The CNN is trained to minimize the distance between same-model
patches, and maximize the distance otherwise. As a result, the noiseprint
accounts for model-related artifacts just like the PRNU accounts for
device-related non-uniformities. However, unlike the PRNU, it is only mildly
affected by residuals of high-level scene content. The experiments show that
the proposed noiseprint-based forgery localization method improves over the
PRNU-based reference
OCR Graph Features for Manipulation Detection in Documents
Detecting manipulations in digital documents is becoming increasingly
important for information verification purposes. Due to the proliferation of
image editing software, altering key information in documents has become widely
accessible. Nearly all approaches in this domain rely on a procedural approach,
using carefully generated features and a hand-tuned scoring system, rather than
a data-driven and generalizable approach. We frame this issue as a graph
comparison problem using the character bounding boxes, and propose a model that
leverages graph features using OCR (Optical Character Recognition). Our model
relies on a data-driven approach to detect alterations by training a random
forest classifier on the graph-based OCR features. We evaluate our algorithm's
forgery detection performance on dataset constructed from real business
documents with slight forgery imperfections. Our proposed model dramatically
outperforms the most closely-related document manipulation detection model on
this task
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
A Novel Fingerprint Encryption Based on Image and Feature Mosaic
Mobile smart devices in the digital era are enhancing personal information security by adopting fingerprint encryption technology, but due to the small size of mobile smart devices, the area of fingerprint image that can be detected is reduced, resulting in the lack of extractable fingerprint feature information, and traditional fingerprint encryption technology is difficult to apply to small area fingerprint images. To solve the application difficulties of small area fingerprint image encryption, a novel small area fingerprint encryption algorithm based on feature and image mosaic was proposed, and the encryption efficiency of the algorithm was verified using FVC2002 and XDFinger database. Results show that the small area fingerprint recognition algorithm based on feature and image mosaic is significantly improved in encryption efficiency, failure capture rate decreases from 36% to 7%, true acceptance rate increases from 44% to 68%, and the feasibility and reliability of the method is verified. Conclusions can promote the application of small area fingerprint encryption technology in mobile smart devices
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
The photo-response non-uniformity (PRNU) is a distinctive image sensor
characteristic, and an imaging device inadvertently introduces its sensor's
PRNU into all media it captures. Therefore, the PRNU can be regarded as a
camera fingerprint and used for source attribution. The imaging pipeline in a
camera, however, involves various processing steps that are detrimental to PRNU
estimation. In the context of photographic images, these challenges are
successfully addressed and the method for estimating a sensor's PRNU pattern is
well established. However, various additional challenges related to generation
of videos remain largely untackled. With this perspective, this work introduces
methods to mitigate disruptive effects of widely deployed H.264 and H.265 video
compression standards on PRNU estimation. Our approach involves an intervention
in the decoding process to eliminate a filtering procedure applied at the
decoder to reduce blockiness. It also utilizes decoding parameters to develop a
weighting scheme and adjust the contribution of video frames at the macroblock
level to PRNU estimation process. Results obtained on videos captured by 28
cameras show that our approach increases the PRNU matching metric up to more
than five times over the conventional estimation method tailored for photos
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