6,442 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
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
Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain
In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided
Evaluation of cosmic ray rejection algorithms on single-shot exposures
To maximise data output from single-shot astronomical images, the rejection
of cosmic rays is important. We present the results of a benchmark trial
comparing various cosmic ray rejection algorithms. The procedures assess
relative performances and characteristics of the processes in cosmic ray
detection, rates of false detections of true objects and the quality of image
cleaning and reconstruction. The cosmic ray rejection algorithms developed by
Rhoads (2000), van Dokkum (2001), Pych (2004) and the IRAF task xzap by
Dickinson are tested using both simulated and real data. It is found that
detection efficiency is independent of the density of cosmic rays in an image,
being more strongly affected by the density of real objects in the field. As
expected, spurious detections and alterations to real data in the cleaning
process are also significantly increased by high object densities. We find the
Rhoads' linear filtering method to produce the best performance in detection of
cosmic ray events, however, the popular van Dokkum algorithm exhibits the
highest overall performance in terms of detection and cleaning.Comment: 12 pages, 4 figures, accepted for publication in PAS
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