83 research outputs found
Image Forgery Localization via Fine-Grained Analysis of CFA Artifacts
In this paper, a forensic tool able to discriminate between original and forged regions in an image captured by a digital camera is presented. We make the assumption that the image is acquired using a Color Filter Array, and that tampering removes the artifacts due to the demosaicking algorithm. The proposed method is based on a new feature measuring the presence of demosaicking artifacts at a local level, and on a new statistical model allowing to derive the tampering probability of each 2 × 2 image block without requiring to know a priori the position of the forged region. Experimental results on different cameras equipped with different demosaicking algorithms demonstrate both the validity of the theoretical model and the effectiveness of our schem
Image Forgery Localization Based on Multi-Scale Convolutional Neural Networks
In this paper, we propose to utilize Convolutional Neural Networks (CNNs) and
the segmentation-based multi-scale analysis to locate tampered areas in digital
images. First, to deal with color input sliding windows of different scales, a
unified CNN architecture is designed. Then, we elaborately design the training
procedures of CNNs on sampled training patches. With a set of robust
multi-scale tampering detectors based on CNNs, complementary tampering
possibility maps can be generated. Last but not least, a segmentation-based
method is proposed to fuse the maps and generate the final decision map. By
exploiting the benefits of both the small-scale and large-scale analyses, the
segmentation-based multi-scale analysis can lead to a performance leap in
forgery localization of CNNs. Numerous experiments are conducted to demonstrate
the effectiveness and efficiency of our method.Comment: 7 pages, 6 figure
Learning Rich Features for Image Manipulation Detection
Image manipulation detection is different from traditional semantic object
detection because it pays more attention to tampering artifacts than to image
content, which suggests that richer features need to be learned. We propose a
two-stream Faster R-CNN network and train it endto- end to detect the tampered
regions given a manipulated image. One of the two streams is an RGB stream
whose purpose is to extract features from the RGB image input to find tampering
artifacts like strong contrast difference, unnatural tampered boundaries, and
so on. The other is a noise stream that leverages the noise features extracted
from a steganalysis rich model filter layer to discover the noise inconsistency
between authentic and tampered regions. We then fuse features from the two
streams through a bilinear pooling layer to further incorporate spatial
co-occurrence of these two modalities. Experiments on four standard image
manipulation datasets demonstrate that our two-stream framework outperforms
each individual stream, and also achieves state-of-the-art performance compared
to alternative methods with robustness to resizing and compression.Comment: CVPR 2018 Camera Read
On the Effectiveness of Image Manipulation Detection in the Age of Social Media
Image manipulation detection algorithms designed to identify local anomalies
often rely on the manipulated regions being ``sufficiently'' different from the
rest of the non-tampered regions in the image. However, such anomalies might
not be easily identifiable in high-quality manipulations, and their use is
often based on the assumption that certain image phenomena are associated with
the use of specific editing tools. This makes the task of manipulation
detection hard in and of itself, with state-of-the-art detectors only being
able to detect a limited number of manipulation types. More importantly, in
cases where the anomaly assumption does not hold, the detection of false
positives in otherwise non-manipulated images becomes a serious problem.
To understand the current state of manipulation detection, we present an
in-depth analysis of deep learning-based and learning-free methods, assessing
their performance on different benchmark datasets containing tampered and
non-tampered samples. We provide a comprehensive study of their suitability for
detecting different manipulations as well as their robustness when presented
with non-tampered data. Furthermore, we propose a novel deep learning-based
pre-processing technique that accentuates the anomalies present in manipulated
regions to make them more identifiable by a variety of manipulation detection
methods. To this end, we introduce an anomaly enhancement loss that, when used
with a residual architecture, improves the performance of different detection
algorithms with a minimal introduction of false positives on the
non-manipulated data.
Lastly, we introduce an open-source manipulation detection toolkit comprising
a number of standard detection algorithms
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