7,446 research outputs found
CNN-based fast source device identification
Source identification is an important topic in image forensics, since it
allows to trace back the origin of an image. This represents a precious
information to claim intellectual property but also to reveal the authors of
illicit materials. In this paper we address the problem of device
identification based on sensor noise and propose a fast and accurate solution
using convolutional neural networks (CNNs). Specifically, we propose a
2-channel-based CNN that learns a way of comparing camera fingerprint and image
noise at patch level. The proposed solution turns out to be much faster than
the conventional approach and to ensure an increased accuracy. This makes the
approach particularly suitable in scenarios where large databases of images are
analyzed, like over social networks. In this vein, since images uploaded on
social media usually undergo at least two compression stages, we include
investigations on double JPEG compressed images, always reporting higher
accuracy than standard approaches
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
IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
With the rapid growth of the Internet-of-Things (IoT), concerns about the
security of IoT devices have become prominent. Several vendors are producing
IP-connected devices for home and small office networks that often suffer from
flawed security designs and implementations. They also tend to lack mechanisms
for firmware updates or patches that can help eliminate security
vulnerabilities. Securing networks where the presence of such vulnerable
devices is given, requires a brownfield approach: applying necessary protection
measures within the network so that potentially vulnerable devices can coexist
without endangering the security of other devices in the same network. In this
paper, we present IOT SENTINEL, a system capable of automatically identifying
the types of devices being connected to an IoT network and enabling enforcement
of rules for constraining the communications of vulnerable devices so as to
minimize damage resulting from their compromise. We show that IOT SENTINEL is
effective in identifying device types and has minimal performance overhead
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