@article{oai:ipsj.ixsq.nii.ac.jp:02001754,
 author = {Jingjing,Rao and Songpon,Teerakanok and Tetsutaro,Uehara and Jingjing Rao and Songpon Teerakanok and Tetsutaro Uehara},
 issue = {4},
 journal = {情報処理学会論文誌},
 month = {Apr},
 note = {With the popularity of digital images in communications and media, image tampering detection has become an important research topic in the field of computer vision. This study uses the DeepLabV3+ model to explore the impact of dilated convolution rate changes and attention mechanisms on the accuracy of image tampering location and particularly emphasizes the application of independently created mobile image tampering datasets in experiments. First, we verified the effectiveness of DeepLabV3+ on basic image segmentation tasks and tried to apply it to more complex image tampering detection tasks. Through a series of experiments, we found that reducing the atrous convolution rate can reduce model complexity and improve training efficiency without significantly affecting accuracy. Furthermore, we integrate channel attention and spatial attention mechanisms, aiming to enhance the model's recognition accuracy of tampered areas. In particular, the mobile datasets we developed contain images shot with smartphones and then tampered with using the phone's built-in editing tools. These datasets play a key role in validating the model's ability to handle real-world tampering scenarios.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.33(2025) (online)
DOI　http://dx.doi.org/10.2197/ipsjjip.33.264
------------------------------, With the popularity of digital images in communications and media, image tampering detection has become an important research topic in the field of computer vision. This study uses the DeepLabV3+ model to explore the impact of dilated convolution rate changes and attention mechanisms on the accuracy of image tampering location and particularly emphasizes the application of independently created mobile image tampering datasets in experiments. First, we verified the effectiveness of DeepLabV3+ on basic image segmentation tasks and tried to apply it to more complex image tampering detection tasks. Through a series of experiments, we found that reducing the atrous convolution rate can reduce model complexity and improve training efficiency without significantly affecting accuracy. Furthermore, we integrate channel attention and spatial attention mechanisms, aiming to enhance the model's recognition accuracy of tampered areas. In particular, the mobile datasets we developed contain images shot with smartphones and then tampered with using the phone's built-in editing tools. These datasets play a key role in validating the model's ability to handle real-world tampering scenarios.
------------------------------
This is a preprint of an article intended for publication Journal of
Information Processing(JIP). This preprint should not be cited. This
article should be cited as: Journal of Information Processing Vol.33(2025) (online)
DOI　http://dx.doi.org/10.2197/ipsjjip.33.264
------------------------------},
 title = {Investigating Atrous Rate Reduction in DeepLabV3+ for Improved Image Tampering Localization: A New Module and Dataset Approach},
 volume = {66},
 year = {2025}
}