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Click the PC Security tab or click the Go to PC Security button. The PC Security panel displays. Click the PC that you want to modify. The Security panel for that computer displays. In the Security Setting line, click the Edit link. Click the

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1 Chapter Cancel anytime Subscribe now Buy Now Price excludes VAT (USA) Tax calculation will be finalised during checkout. Instant access to the full article PDF. Similar content being viewed by others ReferencesAmerini I, Ballan L, Caldelli R, Del Bimbo A, Serra G (2011) A sift-based forensic method for copy–move attack detection and transformation recovery. IEEE Trans. Inf. Forensics Secur. 6:1099–1110Article Google Scholar Amerini I, Ballan L, Caldelli R, Del Bimbo A, Del Tongo L, Serra G (2013) Copy-move forgery detection and localization by means of robust clustering with J-linkage. Signal Process Image Commun 28:659–669Article Google Scholar Amerini I, Uricchio T, Ballan L, Caldelli R (2017) Localization of JPEG double compression through multi-domain convolutional neural networks. In: 2017 IEEE Conf. Comput. Vis. Pattern Recognit. Work., IEEE, pp. 1865–1871Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359Article Google Scholar Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proc. 4th ACM Work. Inf. Hiding Multimed. Secur., pp. 5–10Bi X, Wei Y, Xiao B, Li W (2019) Rru-net: The ringed residual u-net for image splicing forgery detection. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Work., p. 0Bo X, Junwen W, Guangjie L, Yuewei D (2010) Image copy-move forgery detection based on SURF. In: 2010 Int. Conf. Multimed. Inf. Netw. Secur., IEEE, pp. 889–892Bondi L, Lameri S, Güera D, Bestagini P, Delp EJ, Tubaro S (2017) Tampering detection and localization through clustering of camera-based CNN features. In: 2017 IEEE Conf. Comput. Vis. Pattern Recognit. Work., IEEE, pp. 1855–1864Chauhan D, Kasat D, Jain S, Thakare V (2016) Survey on keypoint based copy-move forgery detection methods on image. Procedia Comput Sci 85:206–212Article Google Scholar Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22:1849–1853Article Google Scholar Christlein V, Riess C, Jordan J, Riess C, Angelopoulou E (2012) An evaluation of popular copy-move forgery detection approaches. IEEE Trans. Inf. Forensics Secur. 7:1841–1854Article Google Scholar Columbia Image Splicing Detection Evaluation Dataset, (n.d.) DVMM Lippi G, Henry BM. Chronic obstructive pulmonary disease is associated with severe coronavirus disease 2019 (COVID-19). Respir Med. 2020;167:105941. PubMed PubMed Central Google Scholar Hirsch JS, Ng JH, Ross DW, Sharma P, Shah HH, Barnett RL, et al. Acute kidney injury in patients hospitalized with COVID-19. Kidney Int. 2020;98(1):209–18. CAS PubMed PubMed Central Google Scholar Moon AM, Webb GJ, Aloman C, Armstrong MJ, Cargill T, Dhanasekaran R, et al. High mortality rates for SARS-CoV-2 infection in patients with pre- existing chronic liver disease and cirrhosis: preliminary results from an international registry. J Hepatol. 2020;73(3):705–8. CAS PubMed PubMed Central Google Scholar Fadini GP, Morieri ML, Longato E, Avogaro A. Prevalence and impact of diabetes among people infected with SARS-CoV-2. J Endocrinol Investig. 2020;43(6):867–9. CAS Google Scholar Gao Y, Chen Y, Liu M, Shi S, Tian J. Impacts of immunosuppression and immunodeficiency on COVID-19: a systematic review and meta-analysis. J Inf Secur. 2020;81(2):e93–5. CAS Google Scholar Yang J, Zheng Y, Gou X, Pu K, Chen Z, Guo Q, et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis. Int J Infect Dis. 2020;94:91–5. CAS PubMed PubMed Central Google Scholar Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, et al. Risk factors of critical & mortal COVID-19 cases: a systematic literature review and meta-analysis. J Inf Secur. 2020;81(2):e16–25. CAS Google Scholar Zhang H, Han H, He T, Labbe KE, Hernandez AV, Chen H et al. Clinical Characteristics and Outcomes of COVID-19-Infected Cancer Patients: A Systematic Review and Meta-Analysis. J Natl Cancer Inst. 2020;Online ahead of print.Pranata R, Huang I, Lim MA, Wahjoepramono EJ, July J. Impact of cerebrovascular and cardiovascular diseases on mortality and severity of COVID-19-systematic review, meta-analysis, and meta- regression. J Stroke Cerebrovasc Dis. 2020;29(8):104949. PubMed PubMed Central Google Scholar Aziz F, Mandelbrot D, Singh T, Parajuli S, Garg N, Mohamed M, et al. Early report on published outcomes in kidney transplant recipients compared to nontransplant patients infected with coronavirus disease 2019. Transplant Proc. 2020;52(9):2659–62. CAS PubMed PubMed Central Google Scholar Aziz H, Lashkari N, Yoon YC, Kim J,

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Technol., IEEE, pp. 706–710Qureshi AM, Deriche M (2014) A review on copy-move image forgery detection techniques, multi-conference on systems. Signals & Devices (SSD):11–14Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In: 2016 IEEE Int. Work. Inf. Forensics Secur., IEEE, pp. 1–6Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51:133–162Article Google Scholar Ryu S-J, Kirchner M, Lee M-J, Lee H-K (2013) Rotation invariant localization of duplicated image regions based on Zernike moments. IEEE Trans Inf Forensics Secur 8:1355–1370Article Google Scholar Salloum R, Ren Y, Kuo C-CJ (2018) Image splicing localization using a multi-task fully convolutional network (MFCN). J Vis Commun Image Represent 51:201–209Article Google Scholar Shivakumar BL, Baboo SS (2011) Detection of region duplication forgery in digital images using SURF. Int J Comput Sci Issues 8:199 Google Scholar Tralic D, Zupancic I, Grgic S, M. Grgic (2013) CoMoFoD — new database for copy-move forgery detection. Proceedings ELMAR-2013, pp. 49–54Wang X, Wang H, Niu S, Zhang J (2019) Detection and localization of image forgeries using improved mask regional convolutional neural network. Math Biosci Eng MBE 16:4581–4593Article Google Scholar Wu Y, Abd-Almageed W, Natarajan P (2018) Busternet: Detecting copy-move image forgery with source/target localization. In: Proc. Eur. Conf. Comput. Vis., pp. 168–184Yang J, Ran P, Xiao D, Tan J (2013) Digital image forgery forensics by using undecimated dyadic wavelet transform and Zernike moments. J Comput Inf Syst 9:6399–6408 Google Scholar Zhang J, Ruan Q, Jin Y (2014) Combined SIFT and bi-coherence features to detect image forgery. In: 2014 12th Int. Conf. Signal Process., IEEE, pp. 1859–1863Zhang W, Yang Z, Niu S, Wang J (2016) Detection of copy-move forgery in flat region based on feature enhancement. In: Int. Work. Digit. Watermarking, Springer, pp. 159–171Zhang Y, Goh J, Win LL, Thing VLL (2016) Image Region Forgery Detection: A Deep Learning Approach., SG-CRC. 2016, 1–11Zhao J, Guo J (2013) Passive forensics for copy-move image forgery using a method based on DCT and SVD. Forensic Sci Int 233:158–166Article Google Scholar Zheng Y, Cao Y, Chang C-H (2019) A. Click the PC Security tab or click the Go to PC Security button. The PC Security panel displays. Click the PC that you want to modify. The Security panel for that computer displays. In the Security Setting line, click the Edit link. Click the

Secure Click P95 Respirator Filter, Secure Click D3071 Protection

AbstractIn the digital age, many people have used mobile phones, thus, mobile phones are one of the most commonly used crime tools. Users can take security measures to their mobile devices using various authorization methods such as passwords or screen patterns (touch screen security authentication). The used security measures generally make mobile forensic analysis difficult or even impossible. In order to overcome this problem, a novel intelligent pattern lock detector is presented in this research. The proposed lock detector uses transfer learning to extract deep lightweight features, iterative feature chosen function and a shallow classifier. A feature extraction network has been created by using SqueezeNet and MobileNet-V2, which are among the deep learning architectures in this work. Iterative Minimum Redundancy Maximum Relevance (ImRMR) was used for feature selection. Linear discriminant analysis (LDA) was selected for the classifier. The proposed model has been developed on three image datasets. These datasets are named clean, slightly dirty and medium dirty. 99.75%, 98.55% and 96.50% classification accuracies have been reached on the used three datasets, respectively. The findings clearly denote that the success of the presented deep lightweight features and ImRMR-based detector. Access this article Log in via an institution Subscribe and save Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Subscribe now Buy Now Price excludes VAT (USA) Tax calculation will be finalised during checkout. Instant access to the full article PDF. Similar content being viewed by others Explore related subjects Discover the latest articles, news and stories from top researchers in related subjects. Data availability Three new mobile phone screen patterns datasets were collected and they publicly published. The researchers can download the collected datasets using NotesReferencesAlendal G, Dyrkolbotn GO, Axelsson S (2018) Forensics acquisition — Analysis and circumvention of samsung secure boot enforced common criteria mode. Digit Investig. Google Scholar Garfinkel SL (2010) Digital forensics research: the next 10 years. Digit Investig. Google Scholar Ibrahim TM, Abdulhamid SM, Alarood AA et al (2019) Recent advances in mobile touch screen security authentication methods: a systematic literature review. Comput Secur 85:1–24Article Google Scholar Abdulhamid SM, Waziri VO, Idris I et al (2018) A forensic evidence recovery from mobile device applications. Int J Digit Enterp Technol. Google Scholar Feng T, Liu Z, Kwon KA, et al (2012) Continuous mobile authentication using touchscreen gestures. In: 2012 IEEE International conference on technologies for homeland security, HST 2012Frank M, Biedert R, Ma E et al (2013) Touchalytics: on the applicability of touchscreen input as a behavioral biometric for continuous authentication. IEEE Trans Inf Foren Secur. Google Scholar Shahzad M, Liu AX, Samuel A (2013) Secure unlocking of mobile touch screen devices by simple gestures–You can see I Regul. Pap. 63(3), 401–412 (2015). MathSciNet Google Scholar Flores-Vergara, A., García-Guerrero, E.E., Inzunza-González, E., López-Bonilla, O., Rodríguez-Orozco, E., Cárdenas-Valdez, J.R., Tlelo-Cuautle, E.: Implementing a chaotic cryptosystem in a 64-bit embedded system by using multiple-precision arithmetic. Nonlinear Dyn. 96(1), 497–516 (2019). Google Scholar Lambić, D., Nikolic, M.: Pseudo-random number generator based on discrete-space chaotic map. Nonlinear Dyn. 90(1), 223–232 (2017). MathSciNet Google Scholar He, C., Chen, Z., Wang, L., Wu, X., Liu, T., Long, B.: An algorithm based on 6d fractional order hyperchaotic system and knight tour algorithm to encrypt image. Phys. Scr. 99(5), 055205 (2024). ADS Google Scholar Zhou, S., Qiu, Y., Qi, G., Zhang, Y.: A new conservative chaotic system and its application in image encryption. Chaos, Solitons Fractals 175, 113909 (2023). MathSciNet Google Scholar Jiao, K., Ye, G., Dong, Y., Huang, X., He, J.: Image encryption scheme based on a generalized arnold map and RSA algorithm. Secur. Commun. Netw. 2020, 9721675:1–9721675:14 (2020). W., Korayem, Y., Gabr, M., El-Aasser, M., Maher, E., El-Damak, D., Aboshousha, A.: Anteater: when arnold’s cat meets langton’s ant to encrypt images. IEEE Access 11, 106249–106276 (2023). Google Scholar Yuan Wang, X., Chen, X.: An image encryption algorithm based on dynamic row scrambling and zigzag transformation. Chaos, Solitons Fractals 147, 110962 (2021). H., Xian Nan, S., Hao Liu, Z., Yang, J., Feng, X.: Lossless and lossy remote sensing image encryption-compression algorithm based on deeplabv3+ and 2D cs. Appl. Soft Comput. 159, 111693 (2024). T., Kurihara, K., Kiya, H.: On the security of block scrambling-based etc systems against jigsaw puzzle solver attacks. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE (2017). L., Chen, J., Ma, L., Wang, S.: Cryptanalysis of a chaotic image cipher based on plaintext-related permutation and lookup table. Nonlinear Dyn. 100(4), 3959–3978 (2020). Google Scholar Jolfaei, A., Wu, X.W., Muthukkumarasamy, V.: On the security of permutation-only image encryption schemes. IEEE Trans. Inf. Forensics Secur. 11(2), 235–246 (2016). Google Scholar Chen, J., Chen, L., Zhou, Y.: Cryptanalysis of image ciphers with permutation-substitution network and chaos. IEEE Trans. Circuits Syst. Video Technol. 31(6), 2494–2508 (2020). Google Scholar Zheng, J., Zeng, Q.W.: An image encryption algorithm using a dynamic s-box and chaotic maps. Appl. Intell. 52(13), 15703–15717 (2022). Google Scholar Liu, J., Wang, Y., Liu, Z., Zhu, H.: A chaotic image encryption algorithm based on coupled piecewise sine map and sensitive diffusion structure. Nonlinear Dyn. 104(4), 4615–4633 (2021). Google Scholar Zhu, S., Deng, X.H., Zhang, W., Zhu, C.: Secure image encryption scheme based on a new robust chaotic map and strong s-box. Math. Comput. Simul. 207, 322–346 (2023). MathSciNet Google Scholar Alexan, W., El-Damak, D., Gabr, M.: Image encryption based on fourier-DNA coding for hyperchaotic chen system, chen-based binary quantization

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Addition, it also allows you to compose, decrypt, encrypt, sign, and validate email messages.* Receiving and managing email messages easilyThe Pop3... Category: Software Development / Components & LibrariesPublisher: ComponentSoft, License: Shareware, Price: USD $299.00, File Size: 3.0 MBPlatform: Windows POP3 Pal is a mail client for people on the move. Pop3 Pal is a Mail client for people on the move. It provides an easy way to access your Pop3 Mail from any Windows-based PC. It can also be used for mailbox maintenance - for example, to delete an unwanted large message that is blocking access to your other email.Pop3 Pal lets you read and reply to Mail directly from your Pop3 server. It... Category: Internet / Tools & UtilitiesPublisher: Tech-Pro Limited, License: Shareware, Price: USD $15.00, File Size: 645.1 KBPlatform: Windows YPOPs! YPOPs! is a Windows, Linux, Solaris and Mac program providing Pop3 access to Yahoo! Mail. Yahoo! Mail disabled free access to its Pop3 service in April 2002. This resulted in many people (including myself) to look for alternative free Pop3 services. But this exercise can be very difficult because of the fact that your Yahoo! Mail address could be with... Category: Internet / EmailPublisher: Anuj Seth & K Shyam, License: Freeware, Price: USD $0.00, File Size: 1024.0 KBPlatform: Windows, All Secur-e-mail will encrypt the communication between your e-mail client (e. Secur-e-Mail will encrypt the communication between your e-Mail client (e.g. Outlook) and the Pop3 server using an SSH connection. No server-side installation is needed. The software runs on a Windows system tray. It will enable a local Pop3 server on your computer, which will allow you to setup your e-Mail client to pick up e-Mail locally. Other users... Category: Utilities / Misc. UtilitiesPublisher: ACMEtoolz.com, License: Shareware, Price: USD $19.00, File Size: 510.0 KBPlatform: Windows The Ultimate POP3 is a part of the Mail Component which offers a comprehensive interface for receiving e-mail messages from a server and managing them remotely, all from within your application. The Ultimate Pop3 is a part of the Mail Component which offers a comprehensive interface for receiving e-Mail messages from a server and managing them remotely, all from within your application. In addition, it also allows you to compose, decrypt, encrypt, sign, and validate email messages.* Receiving and managing email messages easilyThe Pop3... Category: Software Development / Components & LibrariesPublisher: ComponentSoft, License: Shareware, Price: USD $299.00, File Size: 3.0 MBPlatform: Windows MailBee POP3 is a powerful and easy to use COM object which enables ASP and Windows applications to receive, parse and delete mail from POP3 servers. MailBee Pop3 is a powerful and easy to use COM object which enables ASP and Windows applications to receive, parse and delete Mail from Pop3 servers. Secure authentication, HTML mails, smart and fast MIME parser, email safety, and much more. Can be used in conjunction with SSL, SMTP and MessageCensor components. Royalty-free distribution. Category: Software Development / Components & LibrariesPublisher: iForum, LLC, License: Shareware, Price: USD $69.00, File Size: 1.8 MBPlatform: Windows Poppy for Windows. Click the PC Security tab or click the Go to PC Security button. The PC Security panel displays. Click the PC that you want to modify. The Security panel for that computer displays. In the Security Setting line, click the Edit link. Click the Click the Update Security tab Click on the Windows Security tab Click the Open Windows Security Button Click the Virus Threat Protection tab Click Scan

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