Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

• Published in: IEEE transactions on image processing Vol. 28; no. 7; pp. 3286 - 3300
• Main Authors: Bappy, Jawadul H., Simons, Cody, Nataraj, Lakshmanan, Manjunath, B. S., Roy-Chowdhury, Amit K.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Back propagation networks; CNN; Confidence; Correlation analysis; decoder; Decoding; encoder; Experimentation; Feature extraction; Feature maps; Forgery; Image compression; Image detection; Image forgery; Image manipulation; Image segmentation; JPEG encoders-decoders; Localization; LSTM; Mapping; Masks; Pixels; Predictions; Resampling; segmentation; Semantics; Shearing; Splicing; tamper localization; Training; Transform coding
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2019.2895466

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.