235 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
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
Effective Image Tampering Localization via Semantic Segmentation Network
With the widespread use of powerful image editing tools, image tampering
becomes easy and realistic. Existing image forensic methods still face
challenges of low accuracy and robustness. Note that the tampered regions are
typically semantic objects, in this letter we propose an effective image
tampering localization scheme based on deep semantic segmentation network.
ConvNeXt network is used as an encoder to learn better feature representation.
The multi-scale features are then fused by Upernet decoder for achieving better
locating capability. Combined loss and effective data augmentation are adopted
to ensure effective model training. Extensive experimental results confirm that
localization performance of our proposed scheme outperforms other
state-of-the-art ones
Progressive Feedback-Enhanced Transformer for Image Forgery Localization
Blind detection of the forged regions in digital images is an effective
authentication means to counter the malicious use of local image editing
techniques. Existing encoder-decoder forensic networks overlook the fact that
detecting complex and subtle tampered regions typically requires more feedback
information. In this paper, we propose a Progressive FeedbACk-enhanced
Transformer (ProFact) network to achieve coarse-to-fine image forgery
localization. Specifically, the coarse localization map generated by an initial
branch network is adaptively fed back to the early transformer encoder layers
for enhancing the representation of positive features while suppressing
interference factors. The cascaded transformer network, combined with a
contextual spatial pyramid module, is designed to refine discriminative
forensic features for improving the forgery localization accuracy and
reliability. Furthermore, we present an effective strategy to automatically
generate large-scale forged image samples close to real-world forensic
scenarios, especially in realistic and coherent processing. Leveraging on such
samples, a progressive and cost-effective two-stage training protocol is
applied to the ProFact network. The extensive experimental results on nine
public forensic datasets show that our proposed localizer greatly outperforms
the state-of-the-art on the generalization ability and robustness of image
forgery localization. Code will be publicly available at
https://github.com/multimediaFor/ProFact
Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks
Freely available and easy-to-use audio editing tools make it straightforward
to perform audio splicing. Convincing forgeries can be created by combining
various speech samples from the same person. Detection of such splices is
important both in the public sector when considering misinformation, and in a
legal context to verify the integrity of evidence. Unfortunately, most existing
detection algorithms for audio splicing use handcrafted features and make
specific assumptions. However, criminal investigators are often faced with
audio samples from unconstrained sources with unknown characteristics, which
raises the need for more generally applicable methods.
With this work, we aim to take a first step towards unconstrained audio
splicing detection to address this need. We simulate various attack scenarios
in the form of post-processing operations that may disguise splicing. We
propose a Transformer sequence-to-sequence (seq2seq) network for splicing
detection and localization. Our extensive evaluation shows that the proposed
method outperforms existing dedicated approaches for splicing detection [3, 10]
as well as the general-purpose networks EfficientNet [28] and RegNet [25].Comment: Accepted at MMFORWILD 2022, ICPR Workshops - Code:
https://faui1-gitlab.cs.fau.de/denise.moussa/audio-splicing-localizatio
Spotting the difference: Context retrieval and analysis for improved forgery detection and localization
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [1], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST)
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