# Operation-wise Attention Network for Tampering Localization Fusion

Polychronis Charitidis  ITI-CERTH
Thessaloniki, Greece
<EMAIL_ADDRESS>Giorgos Kordopatis-Zilos  ITI-CERTH
Thessaloniki, Greece
<EMAIL_ADDRESS>Symeon Papadopoulos  ITI-CERTH
Thessaloniki, Greece
<EMAIL_ADDRESS>Ioannis Kompatsiaris  ITI-CERTH
Thessaloniki, Greece
<EMAIL_ADDRESS>
###### Abstract

In this work, we present a deep learning-based approach for image tampering
localization fusion. This approach is designed to combine the outcomes of
multiple image forensics algorithms and provides a fused tampering
localization map, which requires no expert knowledge and is easier to
interpret by end users. Our fusion framework includes a set of five individual
tampering localization methods for splicing localization on JPEG images. The
proposed deep learning fusion model is an adapted architecture, initially
proposed for the image restoration task, that performs multiple operations in
parallel, weighted by an attention mechanism to enable the selection of proper
operations depending on the input signals. This weighting process can be very
beneficial for cases where the input signal is very diverse, as in our case
where the output signals of multiple image forensics algorithms are combined.
Evaluation in three publicly available forensics datasets demonstrates that
the performance of the proposed approach is competitive, outperforming the
individual forensics techniques as well as another recently proposed fusion
framework in the majority of cases.

###### Index Terms:

Image tampering localization, Deep learning-based fusion, Image forensics

##  I Introduction

Image forensics algorithms are crucial for determining the integrity of
digital images. This is evident by the number of research efforts in the
literature that deal with different types of digital manipulations and traces
[1]. Designing algorithms for detecting and localizing a specific type of
forgery has proven to be challenging, even when evaluating their performance
on controlled datasets and settings [2]. The situation is even more difficult
when dealing with online media. Images are manipulated multiple times with
different tools and are then circulated over the Internet, further undergoing
a variety of transformations (e.g., cropping, re-sizing, re-compression).
Consequently, many methods suffer from low detection accuracy and localization
robustness, presenting noisy outcomes and higher false-positive rates when
applied to new datasets [2]. Due to these observations, many works posit that
there is true benefit in acquiring different reports from independent
algorithms and inspecting the multiple clues in tandem [3, 4, 5, 6]. This is
attributed to the fact that at least one or few of these diverse approaches
are more likely to capture some tampering traces even after an image has
undergone multiple transformations.

Although discovering manipulation traces is desirable, adding more forensics
output visualizations increases the complexity of a media verification tool
that presents the results, especially for non-expert users. The reason for
this is that each algorithm has a different output that requires specific
knowledge for proper interpretation. Consequently, this quickly becomes
overwhelming for the non-expert. As a matter of fact, even forensics experts
may provide wrong or contradicting interpretations of these visualizations
[7].

In this paper, we aim to overcome these limitations, which often exclude non-
expert users from the verification process of suspicious online images. The
main objective is to develop a fully automatic fusion approach using deep
learning, being able to leverage diverse forensics signals that are
complementary to each other, so as to improve the robustness and reliability
of the overall localization system. This final visualization retains the most
important features of the individual algorithms and discards the noise.
Additionally, another important objective is to communicate the tampering
localization results to end users in a manner that is easier to interpret and
requires no additional specialized knowledge. This outcome can empower non-
experts in image forensics, who are interested in image verification, e.g.
fact-checkers and journalists, to actively contribute to image verification
tasks.

The work presented in this paper is conducted in the context of the WeVerify
project [8], which aims to build an open-source platform that engages
communities and citizen journalists alongside newsroom and freelance
journalists for collaborative and decentralized content verification,
tracking, and debunking. For the content verification task, we build on the
existing Image Verification Assistant [9], which is a tool that analyses the
authenticity of images by visualizing the outcomes of various tampering
localization approaches. For the purpose of WeVerify, we have extended this
tool with new state-of-the-art algorithms but also by adding the presented
fusion approach in order to enhance its usability by non-experts, including
fact-checkers, journalists, and citizens.

In this work, we make the following contributions. First, we present a fully
automatic deep learning-based approach that learns how to combine diverse
input signals into a more accurate overall tampering localization
visualization. Second, the final output of our approach is easier to interpret
and requires no specialized knowledge by end users. Finally, the evaluation
results show that the proposed fusion approach largely leads to better results
compared to other approaches.

##  II Related Work

The field of media forensics has become increasingly popular in recent years
[10]. Several fusion approaches have been proposed in the literature, aiming
at combining diverse signals that improve the overall robustness of the
forensics localization maps. The various approaches can be categorized based
on the level at which fusion is performed and the manipulation traces that are
taken into consideration. Frameworks proposed for feature-level fusion often
suffer from drawbacks related to selecting and handling a large number of
features [11, 12]. Additionally, authors in [4] present a framework that fuses
the localization outputs of various forensics algorithms and further refines
the result based on statistical features. The drawback of this method however
is that it has hard-coded heuristics and assumptions about the input tampering
localization algorithms.

Approaches based on measurement-level fusion are best suited for the tampering
detection, and not localization, problem as they provide an overall result
regarding forgeries being present or not [3, 5].

On the other hand, techniques proposed for pixel-level fusion usually involve
utilizing probability output maps and a fusion model to refine the final
output and improve the localization of the tampered region [6]. In recent
years, deep learning shows great power in many research fields, and methods
based on deep Convolutional Neural Networks (CNN) outperform traditional
methods and achieve remarkable success in solving computer vision problems.
For instance, the work in [13] presents a fusion architecture that outperforms
traditional approaches. The difference of this approach is that it fuses
features from different patches of the same image to produce the final
localization map.

##  III Methodology

The main objective of our work is to develop a fully automatic fusion
approach, able to exploit diverse forensics signals from the Image
Verification Assistant and generate a robust and easy-to-interpret
visualization. The current version of Image Verification Assistant contains 11
forensics algorithms with corresponding outputs.

To simplify the training and evaluation of our fusion models, we select a
subset of the forensics algorithms to be considered for fusion. Also, to
provide a direct comparison with another fusion framework [4], we adopt their
algorithm selection based on an evaluation of three publicly available
datasets. These datasets are: (i) The First IFS-TC Image Forensics Challenge
set [14], (ii) the synthetic dataset by Fontani et al. [3], and (iii) the real
cases of the Wild Web dataset [15]. Based on the evaluation results, a set of
five methods were selected as the building blocks of the fusion model:

  * •

ADQ1 [16] and DCT [17] that both base their detection on analysis of the JPEG
compression, in the transform domain;

  * •

BLK [18] and CAGI [19] that base their detection on analysis of the JPEG
compression in the spatial domain;

  * •

Splicebuster [20], a noise-based detector selected as a complementary method
due to its high reported performance and good interpretability of its produced
outputs.

In this work, we adopt a deep learning-based fusion approach for the following
reasons. First, we aspire to develop a fully automatic approach without the
need for heuristic tuning or manual intervention, as for instance in the case
of [4]. Second, the complex and diverse nature of the input signal calls for
an effective approach to automatically extract the most important features,
which deep learning excels at [21]. Finally, the availability of large-scale
datasets, which are required by deep learning approaches makes the training of
a deep learning-based model feasible.

For the fusion architecture, we initially consider two different deep learning
architectures. The first model we adopt is U-Net [22]. U-Net is a
convolutional neural network that was initially applied for semantic
segmentation in a medical context in order to localize tumors in the lungs or
brain, but nowadays, it has got a much broader application field. The main
idea is to supplement a usual contracting network by successive layers, where
pooling operations are replaced by up-sampling operators. Hence these layers
increase the resolution of the output. A successive convolutional layer can
then learn to assemble a precise output based on this information. One
important feature in U-Net is that there are a large number of feature
channels in the up-sampling part, which allow the network to propagate context
information to higher resolution layers. Consequently, the expansive path is
roughly symmetric to the contracting part and yields a U-shaped architecture.
The network only uses the valid part of each convolution without any fully
connected layers. The U-Net architecture has a lot of variants. For the fusion
task, we use _Eff-Unet_ [23], a variant of U-Net architecture that uses
_EfficientNet_ [24] as the encoder part. To adapt the _Eff-Unet_ to our fusion
task, we use the _EfficientNet-B4_ variant as the encoder. We refer to this
model as _Eff-B4-Unet_.

Figure 1: The architecture of adapted operation-wise attention layer,
comprising an attention and operation layer, a concatenation operation, and 1
× 1 convolution.

The second model that we employ is a simple architecture of neural networks
that was proposed for the problem of image restoration [25]. It performs
multiple operations in parallel, which are weighted by an attention mechanism
to enable the selection of proper operations depending on the input. The layer
can be stacked to form a deep network, which is differentiable and thus can be
trained in an end-to-end fashion by gradient descent. This architecture is
suitable for our problem because it uses attention to capture important
features by examining which operations are the most beneficial, depending on
the input signal. Another important aspect of this architecture is that it can
learn to attend low-level features, which is important for the fusion task, as
semantic or high-level representations are often not relevant or useful for
the problem. The network includes operations like, dilated convolutions,
separable convolutions, and pooling with various kernel sizes and rates, which
are weighted by an attention mechanism. To adapt this architecture to our
needs, we replaced dilated convolutions with simple convolutions as the former
are better suited for the image restoration task and exhibit lower validation
performance in preliminary experiments. Additionally, we reduce the number of
stacked Operation-wise Attention (OwA) layers from 10 to 4, in order to avoid
overfitting during training. Also, in our adaptation, each operation-wise
attention layer consists of one operation layer, in contrast with the original
implementation that stacks 4 consecutive operation layers. Figure 1 shows the
modified operation-wise attention layer. In the operation layer, we use simple
convolutions with filter sizes 1 × 1, 3 × 3, 5 × 5, 7 × 7, average and max
pooling with a 3 × 3 receptive field. We refer to this model as OwAF.

##  IV Experimental study

In this section, we compare these two deep learning approaches and we use the
best performing in further evaluations, in order to compare it with another
fusion approach against two publicly available image forensics datasets.

###  IV-A Training and evaluation setup

#### Training set

To train the deep learning models that we describe in Section III, we need to
use an image forensics dataset for training. For the model training in the
fusion task, we generate a set of tampering localization outputs by applying
the forensics algorithms for each image in the dataset.

Currently, there are not many large-scale forensics datasets available, and
this is mainly due to the fact that their generation is very time-consuming.
On the other hand, it is easy to generate synthetic forgeries in images. For
this work, we use the DEFACTO dataset [26], which contains more than 150,000
images with synthetic forgeries and their corresponding ground truth masks.
The dataset contains four manipulation types, including splicing, copy-move,
object removal, and face morphing. For our task, we use only images from the
first three manipulation types. We randomly compile 15,000 images for
training, 1000 for test, and 1000 for validation. For every input image in
each set, we produce a set of different tampering localization maps obtained
by the selected detection methods. This means that the produced training set
for the fusion model consists of $15,000\times 5$ tampering localization maps
and corresponding ground truth masks for every image. We concatenate the
forensics maps in the channel axis to generate training samples.

#### Parameter selection

We use the Adam optimizer [27] with an initial learning rate of 0.001, and we
minimize the pixel-size binary cross-entropy loss. To achieve this, the final
layer of both architectures has a sigmoid activation function. Ground truth
masks are also in the [0,1] range, having zeros for the pristine parts of the
image and ones for the tampered regions. The training batch size is set to 4.
We train the model for 20 epochs, and we save only the weights of the model
that has the lowest validation error. The validation error is derived from the
evaluation of the images in the DEFACTO validation set. For faster training,
we only use 300 images of the validation set. We also reduce the learning rate
by a factor of 10 if the validation error does not improve for 5 epochs.

#### Datasets

To evaluate the models, we use three datasets. First, we evaluate the proposed
deep learning models against the DEFACTO test dataset that contains 1000
images. Then, we choose the best of the two models and compare them to another
previously proposed model [4]. This comparison is performed on two different
forensics datasets. The first is the IFS-TC Image Forensics Challenge dataset
[14], which contains 450 images with forgeries and is designed to serve as a
realistic benchmark. Focusing on splicing tampering localization, we excluded
cases that were produced by copy-move operations resulting in a set of 306
forgery cases produced through splicing operations only. Tampered images in
this dataset are accompanied by ground-truth maps. The second dataset is the
CASIA V2.0 dataset [28] that contains 5,123 realistically tampered color
images of varying sizes. This dataset does not come with ground truth maps. In
order for us to be able to perform localization tests, we take the ground
truth masks that were produced in another work [4], where 2,195 reliable
ground truth maps were manually produced through semi-automated procedures
involving image differencing, thresholding, and morphological operations.

#### Evaluation metrics:

We use the F1 score, both the macro average and the F1 for the tampered class,
and the average Intersection over Union (IoU) metric.

Figure 2: Various examples of tampering localization outputs using input
images from the DEFACTO test dataset and fusion visualizations from the OwAF
model; GT column refers to ground truth masks.

###  IV-B Results

In the first experiment, we investigate the performance of the two proposed
fusion models. Table I shows the evaluation results of the two proposed models
in the DEFACTO test dataset as well as results from all individual algorithms.

TABLE I: Evaluation metrics on DEFACTO test dataset for the proposed fusion models and the individual localization algorithms. The binarization threshold is set to 0.5 Models | Macro-F1 | F1 (tampered) | IoU
---|---|---|---
BLK [18] | 0.463 | 0.090 | 0.053
ADQ1 [16] | 0.573 | 0.221 | 0.123
CAGI [19] | 0.479 | 0.142 | 0.072
DCT [17] | 0.509 | 0.186 | 0.101
Splicebuster [20] | 0.554 | 0.153 | 0.087
Eff-B4-Unet | 0.908 | 0.798 | 0.690
OwAF | 0.912 | 0.829 | 0.707

Figure 3: Successful examples of tampering localization outputs from the OwAF
model and visual comparisons with the individual methods and Iakovidou et al.
[4]; for the first four rows, images are taken from the CASIA v2 dataset, for
the last four rows from the First IFS-TC Image Forensics Challenge dataset.

We can see that the OwAF network outperforms the Eff-B4-Unet in every
evaluation metric. Evaluation results for individual algorithms are very low
when compared to the fusion approaches. This can be attributed to the fact
that certain algorithms work well on specific manipulation types and images.
Also, the 0.5 binarization threshold may impair the evaluation performance of
many algorithms, as many algorithms might operate on lower thresholds.
Averaging the results from many binarization ranges did not significantly
improve the evaluation results, nonetheless. The best performing individual
model is ADQ1. Another important observation is that the macro-averaged F1
score is higher compared to the F1 score for the tampered class for all
evaluations. This means that the models can predict the pristine regions more
accurately than the tampered ones. In terms of intersection over union (IoU)
evaluation, we can see that the OwAF model still outperforms Eff-B4-Unet.

Figure 2 shows random examples from the DEFACTO test dataset. The first column
includes the input images. The next five columns show the outputs of the
individual tampering localization algorithms. The final two columns show the
ground truth mask, which reveals the actual location of the forgery and the
fusion result of the OwAF model that combines the localization outputs,
respectively. It is evident from these examples that the fusion architecture
learned to combine the diverse signals in order to localize the tampered
region. One interesting observation is that for each input example, there are
usually different algorithms that better localize the forgery. It seems that
the fusion model learned to detect proper signals that contribute to a correct
localization. For example, in the first row, Splicebuster and CAGI spot the
tampering, but in row three, ADQ1 and DCT do so. In both cases, the fusion
model has identified these signals and provides a correct result.

To further investigate the fusion performance, we compare our best performing
approach with another fusion framework proposed by [4]. For evaluation, we use
the two datasets described above (CASIA v2 and IFS-TC) in order to examine the
generalization capabilities of the fusion model that was trained with the
DEFACTO dataset. CASIA v2 is the first evaluation dataset we use. Table II
shows the evaluation results. The binarization threshold is set to 0.5.

TABLE II: Evaluation results of the CASIA v2 and First IFS-TC Image Forensics Challenge datasets. The binarization threshold for the metric calculation is set to 0.5 Dataset | Models | Macro-F1 | F1 (tampered) | IoU
---|---|---|---|---
CASIA v2 | BLK [18] | 0.509 | 0.140 | 0.089
ADQ1 [16] | 0.573 | 0.217 | 0.130
CAGI [19] | 0.502 | 0.162 | 0.094
DCT [17] | 0.546 | 0.196 | 0.113
Splicebuster [20] | 0.576 | 0.163 | 0.093
Iakovidou et al. [4] | 0.598 | 0.253 | 0.166
OwAF | 0.611 | 0.270 | 0.172
IFS-TC | BLK [18] | 0.459 | 0.092 | 0.063
ADQ1 [16] | 0.485 | 0.115 | 0.076
CAGI [19] | 0.506 | 0.135 | 0.091
DCT [17] | 0.467 | 0.106 | 0.065
Splicebuster [20] | 0.560 | 0.199 | 0.129
Iakovidou et al. [4] | 0.549 | 0.171 | 0.112
OwAF | 0.529 | 0.140 | 0.106

We can observe slightly better performance in every metric from individual
models compared to those in Table I. This means that this dataset contains
images with manipulations that can be localized better by the individual
algorithms. ADQ1 and DCT are still the best performing individual approaches.
Regarding the fusion methods evaluation, we can see that our approach
outperforms the competing fusion framework [4]. One notable observation is
that the performance of OwAF is significantly worse than the evaluation
results that are reported in Table I. This is a clear indication that our
trained models have overfitted to the training set manipulations. The fusion
model possibly learned to localize specific forgeries, like shapes and
patterns from the outputs of individual algorithms that frequently appear in
the DEFACTO dataset. For example, the DEFACTO dataset contains synthetic
manipulations on specific objects such as traffic signs, birds, airplanes,
humans (Figure 2). Yet, the proposed approach is still better than individual
algorithms and also outperforms the fusion framework of [4] in terms of both
F1 score and IoU.

Evaluation results on the First IFS-TC Image Forensics Challenge dataset are
also reported in Table II. In these results, a significant decrease in the
performance of the individual algorithms can be observed. One exception is the
Spicebuster performance, which increased compared to previous evaluations.
Splicebuster even outperforms both fusion approaches. Iakovidou et al. [4]
also achieve marginally better performance than our fusion model in this
dataset. One possible explanation is that our fusion model learned to focus
more on the individual localization maps that achieved better performance in
the training set, namely the ADQ1 and DCT. This can also be verified from the
CASIA v2 evaluation results, where ADQ1 and DCT are still the best individual
approaches, and our approach outperforms all others. On the contrary, in the
First IFS-TC Image Forensics Challenge case, the best performing individual
algorithm is Splicebuster and this possibly justifies the poor performance of
OwAF.

Figure 3 shows some successful examples of tampering localization outputs
produced by the fusion model and the individual methods from the two
evaluation datasets. In most examples, ADQ1 and DCT visualization better
localize the tampering. There are also cases where Splicebuster and CAGI
better localize the forgeries. Additionally, in these specific examples, we
can see that the visual result from the method of Iakovidou et al. [4] is very
poor for the CASIA v2 samples (first four). The qualitative results are
improved for the IFS-TC samples (last four), which is expected as this
approach overall performs better in this dataset.

From our experiments, it is evident that the main challenge of the proposed
approach stems from overfitting to the training data. Although OwAF can
outperform individual forensics algorithms or other fusion models, we observe
a lack of generalization to unseen manipulations. Namely, we get poor
predictions for datasets that have different types of manipulations compared
to those that appeared in the training dataset. Additionally, the low
evaluation performance of individual models is a major indication that the
forgery localization problem is very difficult and is even more challenging to
design a general fusion solution for images in the wild. These observations
are also reported by other researchers [2].

##  V Conclusions

The main objective of our work has been to develop a fully automatic fusion
approach, able to exploit diverse signals from various forensics algorithms.
The fusion outcome would communicate the tampering localization results to end
users in a manner that is easier to interpret and requires no additional
specialized knowledge.

The reported experimental results are promising and in many cases outperform
the individual forensics techniques and a competing fusion approach. An
important limitation of this work is the generalization ability of the fusion
model and stems from the general limitations of the underlying supervised
learning scheme and the insufficient representativeness of the dataset that
was used for training.

In the future, we plan to work on these generalization limitations. To this
end, we will try to increase the size of the training dataset and include
different manipulations from other synthetic datasets. Additionally, we will
experiment with task-specific regularization techniques, like feature map
dropout. Finally, another interesting research direction is to experiment with
multi-stream fusion architectures that besides the forensics localization
maps, will extract features from the input image itself.

## Acknowledgments

This work has been supported by the WeVerify H2020 project, partially funded
by the European Commission under contract number 825297.

## References

  * [1] P. Korus, “Digital image integrity–a survey of protection and verification techniques,” _Digital Signal Processing_ , 2017.
  * [2] M. Zampoglou, S. Papadopoulos, and Y. Kompatsiaris, “Large-scale evaluation of splicing localization algorithms for web images,” _Multimedia Tools and Applications_ , 2017.
  * [3] M. Fontani, T. Bianchi, A. De Rosa, A. Piva, and M. Barni, “A framework for decision fusion in image forensics based on dempster–shafer theory of evidence,” _IEEE transactions on Information Forensics and Security_ , 2013\.
  * [4] C. Iakovidou, S. Papadopoulos, and Y. Kompatsiaris, “Knowledge-based fusion for image tampering localization,” in _IFIP International Conference on Artificial Intelligence Applications and Innovations_ , 2020.
  * [5] M. Kaur and S. Gupta, “A fusion framework based on fuzzy integrals for passive-blind image tamper detection,” _Cluster Computing_ , 2019.
  * [6] H. Li, W. Luo, X. Qiu, and J. Huang, “Image forgery localization via integrating tampering possibility maps,” _IEEE Transactions on Information Forensics and Security_ , 2017.
  * [7] “Expert criticizes allegations of russian mh17 manipulation,” accessed on Jan 2121\. [Online]. Available: https://www.spiegel.de/international/world/expert-criticizes-allegations-of-russian-mh17-manipulation-a-1037125.html
  * [8] Z. Marinova, J. Spangenberg, D. Teyssou, S. Papadopoulos, N. Sarris, A. Alaphilippe, and K. Bontcheva, “Weverify: Wider and enhanced verification for you project overview and tools,” in _2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)_, 2020.
  * [9] M. Zampoglou, S. Papadopoulos, Y. Kompatsiaris, R. Bouwmeester, and J. Spangenberg, “Web and social media image forensics for news professionals,” in _Proceedings of the International AAAI Conference on Web and Social Media_ , 2016.
  * [10] L. Verdoliva, “Media forensics and deepfakes: an overview,” _IEEE Journal of Selected Topics in Signal Processing_ , vol. 14, no. 5, pp. 910–932, 2020.
  * [11] M. Barni and A. Costanzo, “A fuzzy approach to deal with uncertainty in image forensics,” _Signal Processing: Image Communication_ , 2012.
  * [12] G. Chetty and M. Singh, “Nonintrusive image tamper detection based on fuzzy fusion,” _International Journal of Computer Science and Network Security_ , 2010.
  * [13] B. Liu and C.-M. Pun, “Deep fusion network for splicing forgery localization,” in _Proceedings of the European Conference on Computer Vision (ECCV) Workshops_ , 2018.
  * [14] “Report on the ieee-ifs challenge,” accessed on Mar 2016. [Online]. Available: http://ifc.recod.ic.unicamp.br/
  * [15] M. Zampoglou, S. Papadopoulos, and Y. Kompatsiaris, “Detecting image splicing in the wild (web),” in _2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)_, 2015.
  * [16] Z. Lin, J. He, X. Tang, and C.-K. Tang, “Fast, automatic and fine-grained tampered jpeg image detection via dct coefficient analysis,” _Pattern Recognition_ , 2009.
  * [17] S. Ye, Q. Sun, and E.-C. Chang, “Detecting digital image forgeries by measuring inconsistencies of blocking artifact,” in _2007 IEEE International Conference on Multimedia and Expo_ , 2007.
  * [18] W. Li, Y. Yuan, and N. Yu, “Passive detection of doctored jpeg image via block artifact grid extraction,” _Signal Processing_ , 2009.
  * [19] C. Iakovidou, M. Zampoglou, S. Papadopoulos, and Y. Kompatsiaris, “Content-aware detection of jpeg grid inconsistencies for intuitive image forensics,” _Journal of Visual Communication and Image Representation_ , 2018\.
  * [20] D. Cozzolino, G. Poggi, and L. Verdoliva, “Splicebuster: A new blind image splicing detector,” in _2015 IEEE International Workshop on Information Forensics and Security (WIFS)_ , 2015.
  * [21] A. Voulodimos, N. Doulamis, A. Doulamis, and E. Protopapadakis, “Deep learning for computer vision: A brief review,” _Computational intelligence and neuroscience_ , 2018.
  * [22] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in _International Conference on Medical image computing and computer-assisted intervention_ , 2015.
  * [23] B. Baheti, S. Innani, S. Gajre, and S. Talbar, “Eff-unet: A novel architecture for semantic segmentation in unstructured environment,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops_ , 2020.
  * [24] M. Tan and Q. Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” in _International Conference on Machine Learning_ , 2019\.
  * [25] M. Suganuma, X. Liu, and T. Okatani, “Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions,” in _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , 2019.
  * [26] G. Mahfoudi, B. Tajini, F. Retraint, F. Morain-Nicolier, J. L. Dugelay, and P. Marc, “Defacto: Image and face manipulation dataset,” in _2019 27th European Signal Processing Conference (EUSIPCO)_ , 2019.
  * [27] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in _3rd International Conference on Learning Representations_ , 2015.
  * [28] J. Dong, W. Wang, and T. Tan, “Casia image tampering detection evaluation database,” in _2013 IEEE China Summit and International Conference on Signal and Information Processing_ , 2013.