MATLAB PROJECT
Hybrid LSTM and
Encoder–Decoder Architecture for Detection of Image Forgeries
Abstract:
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 that utilizes resampling features, long short-term
memory (LSTM) cells, and an encoder-decoder network to segment out manipulated
regions from non-manipulated ones. Resampling features are used to capture
artifacts, such as 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 the manipulated and non-manipulated regions by incorporating the
encoder and LSTM network. Finally, the decoder network learns the mapping from low-resolution
feature maps to pixel-wise predictions for image tamper localization. With the
predicted mask provided by the final layer (softmax) of the proposed
architecture, end-to-end training is performed to learn the network parameters
through back-propagation using the 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 the pixel level
with high precision, which is demonstrated through rigorous experimentation on
three diverse datasets.
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