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An image storage duplication detection method using recurrent learning for smart application services

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Abstract

Smart and intelligent application services rely on textual and visualization information for meeting user demands. Regardless of the textual data, visualization information requires large storage volumes due to its size and density. Visual information such as images and video occupies large storage space in a replicated manner. The retrieval complexity of such image inputs is considerably high, increasing the service latency. This paper introduces a novel dimension-correlated de-duplication method (DCDDM) for reducing the image replications of identical application services. The proposed method identifies the pixel dimensions of the input irrespective of its scale before storage. The pre-storage analysis correlates the dimension vectors of the stored and analyzed image using the identical feature and null properties. A trained learning paradigm is used for recurrent verification of the identical and null properties of stored and input images. The recurrent instance identifies un-matching vectors for preventing replications in storage and reducing overflow storage utilization. The proposed strategy reduces the storage rate by 16.49%, increases the detection ratio by 8.9%, reduces analysis time by 10.36%, and reduces variances by 9.04% for the various features evaluated. The findings show that the proposed method is reliable for usage in smart application services to reduce storage requirements and eliminate data duplication.

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References

  1. Tyj NM (2019) Adaptive deduplication of virtual machine images using AKKA stream to accelerate live migration process in cloud environment. J Cloud Comput 8(1):1–12

    Article  Google Scholar 

  2. Kaur R, Chana I, Bhattacharya J (2018) Data deduplication techniques for efficient cloud storage management: a systematic review. J Supercomput 74(5):2035–2085

    Article  Google Scholar 

  3. Balamurugan NM, Adimoolam M, John A (2021) A novel efficient algorithm for duplicate video comparison in surveillance video storage systems. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03119-7

    Article  Google Scholar 

  4. Hernández-Cabronero M, Sanchez V, Blanes I, Auli-Llinas F, Marcellin MW, Serra-Sagristà J (2018) Mosaic-based color-transform optimization for lossy and lossy-to-lossless compression of pathology whole-slide images. IEEE Trans Med Imaging 38(1):21–32

    Article  Google Scholar 

  5. Zheng L, Zhang Y, Thing VL (2019) A survey on image tampering and its detection in real-world photos. J Vis Commun Image Represent 58:380–399

    Article  Google Scholar 

  6. Wang J, Li M, Wang S, Menzies T, Wang Q (2019) Images don’t lie: duplicate crowdtesting reports detection with screenshot information. Inf Softw Technol 110:139–155

    Article  Google Scholar 

  7. Morra L, Lamberti F (2019) Benchmarking unsupervised near-duplicate image detection. Expert Syst Appl 135:313–326

    Article  Google Scholar 

  8. Chen M, Li Y, Zhang Z, Hsu CH, Wang S (2019) Erratum to: real-time, large-scale duplicate image detection method based on multi-feature fusion. J Real Time Image Proc 16(5):1881–1881

    Article  Google Scholar 

  9. Yang Y, Tian Y, Huang T (2019) Multiscale video sequence matching for near-duplicate detection and retrieval. Multimed Tools Appl 78(1):311

    Article  Google Scholar 

  10. Kim H, Sohn S, Kim J (2019) Revisiting Gist-PCA hashing for near duplicate image detection. J Signal Process Syst 91(6):575–586

    Article  Google Scholar 

  11. Agarwal R, Verma OP (2021) Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching. Evol Syst : 1–15

  12. Jiang F, Gong M, Zhan T, Fan X (2019) A semisupervised GAN-based multiple change detection framework in multi-spectral images. IEEE Geosci Remote Sens Lett 17(7):1223–1227

    Article  Google Scholar 

  13. Hammad R (2020) Image forgery detection using image similarity. Multimed Tools Appl 79(39):28643–28659

    Google Scholar 

  14. Dixit R, Naskar R (2019) Region duplication detection in digital images based on centroid linkage clustering of key–points and graph similarity matching. Multimed Tools Appl 78(10):13819–13840

    Article  Google Scholar 

  15. Saheed YK, Arowolo MO (2021) Efficient cyber attack detection on the internet of medical things-smart environment based on deep recurrent neural network and machine learning algorithms. IEEE Access 9:161546–161554

    Article  Google Scholar 

  16. Huang F, Zhou Z, Yang CN, Liu X, Wang T (2019) Original image tracing with image relational graph for near-duplicate image elimination. Int J Comput Sci Eng 18(3):294–304

    Google Scholar 

  17. Wang XY, Jiao LX, Wang XB, Yang HY, Niu PP (2018) A new keypoint-based copy-move forgery detection for color image. Appl Intell 48(10):3630–3652

    Article  Google Scholar 

  18. Gani G, Qadir F (2019) A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns. Evol Syst : 1–15

  19. Zhou Z, Mu Y, Wu QJ (2019) Coverless image steganography using partial-duplicate image retrieval. Soft Comput 23(13):4927–4938

    Article  Google Scholar 

  20. El Biach FZ, Iala I, Laanaya H, Minaoui K (2021) Encoder-decoder based convolutional neural networks for image forgery detection. Multimed Tools Appl : 1–18

  21. Yang HY, Niu Y, Jiao LX, Liu YN, Wang XY, Zhou ZL (2018) Robust copy-move forgery detection based on multi-granularity superpixels matching. Multimed Tools Appl 77(11):13615–13641

    Article  Google Scholar 

  22. http://www.vision.caltech.edu/pmoreels/Datasets/Home_Objects_06/#Download

  23. Yafeng L (2021) A robust near-duplicate images detection approach with ordinal measure. In 2021 5th International Conference on Robotics and Automation Sciences (ICRAS). IEEE, pp 226–229

  24. Li Y (2021) A fast algorithm for near-duplicate image detection. In 2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID). IEEE, pp 360–363

  25. Mageshkumar N, Lakshmanan L (2022) An improved secure file deduplication avoidance using CKHO based deep learning model in a cloud environment. J Supercomput 78:1–27

    Article  Google Scholar 

  26. Krishnasamy V, Venkatachalam S (2021) An efficient data flow material model based cloud authentication data security and reduce a cloud storage cost using Index-Level Boundary Pattern Convergent Encryption algorithm. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.04.303

    Article  Google Scholar 

  27. Sadeghi S, Dadkhah S, Jalab HA, Mazzola G, Uliyan D (2018) State of the art in passive digital image forgery detection: copy-move image forgery. Pattern Anal Appl 21(2):291–306

    Article  MathSciNet  Google Scholar 

  28. Hu W, Fan Y, Xing J, Sun L, Cai Z, Maybank S (2018) Deep constrained siamese hash coding network and load-balanced locality-sensitive hashing for near duplicate image detection. IEEE Trans Image Process 27(9):4452–4464

    Article  MathSciNet  MATH  Google Scholar 

  29. Zhou Z, Wu QJ, Wan S, Sun W, Sun X (2020) Integrating SIFT and CNN feature matching for partial-duplicate image detection. IEEE Trans Emerg Top Comput Intell 4(5):593–604

    Article  Google Scholar 

  30. Yang M, Jiao L, Liu F, Hou B, Yang S (2019) Transferred deep learning-based change detection in remote sensing images. IEEE Trans Geosci Remote Sens 57(9):6960–6973

    Article  Google Scholar 

  31. Kaur R, Bhattacharya J, Chana I (2022) Deep CNN based online image deduplication technique for cloud storage system. Multimed Tools Appl 81:1–34

    Article  Google Scholar 

  32. Makkar A, Kumar N (2021) PROTECTOR: an optimized deep learning-based framework for image spam detection and prevention. Future Gener Comput Syst 125:41–58

    Article  Google Scholar 

  33. Mehta P (2021) Detection of near-duplicate images using statistical texture features. In 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE, pp 1–8

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IFET College of Engineering, Villupuram, Tamilnadu, India

    S. Usharani

  2. Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India

    K. Dhanalakshmi

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  1. S. Usharani
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  2. K. Dhanalakshmi
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Correspondence to S. Usharani.

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Author S. Usharani declares that I have no conflict of interest. Author K. Dhanalakshmi declares that she has no conflict of interest.

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Usharani, S., Dhanalakshmi, K. An image storage duplication detection method using recurrent learning for smart application services. J Supercomput 79, 11328–11354 (2023). https://doi.org/10.1007/s11227-023-05042-4

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