230 research outputs found
Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection
Local descriptors based on the image noise residual have proven extremely
effective for a number of forensic applications, like forgery detection and
localization. Nonetheless, motivated by promising results in computer vision,
the focus of the research community is now shifting on deep learning. In this
paper we show that a class of residual-based descriptors can be actually
regarded as a simple constrained convolutional neural network (CNN). Then, by
relaxing the constraints, and fine-tuning the net on a relatively small
training set, we obtain a significant performance improvement with respect to
the conventional detector
Distinguishing Computer-generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Computer-generated graphics (CGs) are images generated by computer software.
The~rapid development of computer graphics technologies has made it easier to
generate photorealistic computer graphics, and these graphics are quite
difficult to distinguish from natural images (NIs) with the naked eye. In this
paper, we propose a method based on sensor pattern noise (SPN) and deep
learning to distinguish CGs from NIs. Before being fed into our convolutional
neural network (CNN)-based model, these images---CGs and NIs---are clipped into
image patches. Furthermore, three high-pass filters (HPFs) are used to remove
low-frequency signals, which represent the image content. These filters are
also used to reveal the residual signal as well as SPN introduced by the
digital camera device. Different from the traditional methods of distinguishing
CGs from NIs, the proposed method utilizes a five-layer CNN to classify the
input image patches. Based on the classification results of the image patches,
we deploy a majority vote scheme to obtain the classification results for the
full-size images. The~experiments have demonstrated that (1) the proposed
method with three HPFs can achieve better results than that with only one HPF
or no HPF and that (2) the proposed method with three HPFs achieves 100\%
accuracy, although the NIs undergo a JPEG compression with a quality factor of
75.Comment: This paper has been published by Sensors. doi:10.3390/s18041296;
Sensors 2018, 18(4), 129
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
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