189 research outputs found
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
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
Exposing Fake Images with Forensic Similarity Graphs
We propose new image forgery detection and localization algorithms by
recasting these problems as graph-based community detection problems. To do
this, we introduce a novel abstract, graph-based representation of an image,
which we call the Forensic Similarity Graph, that captures key forensic
relationships among regions in the image. In this representation, small image
patches are represented by graph vertices with edges assigned according to the
forensic similarity between patches. Localized tampering introduces unique
structure into this graph, which aligns with a concept called ``community
structure'' in graph-theory literature. In the Forensic Similarity Graph,
communities correspond to the tampered and unaltered regions in the image. As a
result, forgery detection is performed by identifying whether multiple
communities exist, and forgery localization is performed by partitioning these
communities. We present two community detection techniques, adapted from
literature, to detect and localize image forgeries. We experimentally show that
our proposed community detection methods outperform existing state-of-the-art
forgery detection and localization methods, which do not capture such community
structure.Comment: 16 pages, under review at IEEE Journal of Selected Topics in Signal
Processin
Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
With advanced image journaling tools, one can easily alter the semantic
meaning of an image by exploiting certain manipulation techniques such as
copy-clone, object splicing, and removal, which mislead the viewers. In
contrast, the identification of these manipulations becomes a very challenging
task as manipulated regions are not visually apparent. This paper proposes a
high-confidence manipulation localization architecture which utilizes
resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder
network to segment out manipulated regions from non-manipulated ones.
Resampling features are used to capture artifacts like JPEG quality loss,
upsampling, downsampling, rotation, and shearing. The proposed network exploits
larger receptive fields (spatial maps) and frequency domain correlation to
analyze the discriminative characteristics between manipulated and
non-manipulated regions by incorporating encoder and LSTM network. Finally,
decoder network learns the mapping from low-resolution feature maps to
pixel-wise predictions for image tamper localization. With predicted mask
provided by final layer (softmax) of the proposed architecture, end-to-end
training is performed to learn the network parameters through back-propagation
using ground-truth masks. Furthermore, a large image splicing dataset is
introduced to guide the training process. The proposed method is capable of
localizing image manipulations at pixel level with high precision, which is
demonstrated through rigorous experimentation on three diverse datasets
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