With Recent And Rapid Advancements In Communication Technologies, Digital Signals Can Be Transmitted Over The Internet With Convenience. These Advancements Have Brought Many Advantages But At The Same Time There Are Several Hazards And Risks That Need To Be Considered As Well. Technologies Such As Telemedicine Are Emerging Day By Day And Ensuring Medical Data Security Is Becoming A Challenge. Recently, Capturing Medical Data Has Appeared As A Major Cybercrime. If Such Sensitive Data Is Stolen Or Captured, Then It Can Result In Violation Of Basic Patient Rights. Confidentiality In Medical Reports Must Be Kept Intact In Order To Ensure Trust Among Patients And Health Care Institutions. —Recently, Image Steganography Is Being Considered As An Alternative Method For Securing Medical Data To Avoid Medical Related Cybercrimes. This Paper Proposes A New Image Steganography Approach For Securing Medical Data. Swapped Huffman Tree Coding Is Used To Apply Lossless Compression And Manifold Encryption To The Payload Before Embedding Into The Cover Image. Additionally, Only Edge Regions Of The Cover Image Are Used To Embed The Secret Data Which Offers High Imperceptibility.
Recently, The Convolutional Neural Network Has Brought Impressive Improvements For Object Detection. However, Detecting Tiny Objects In Large-scale Remote Sensing Images Still Remains Challenging. First, The Extreme Large Input Size Makes The Existing Object Detection Solutions Too Slow For Practical Use. Second, The Massive And Complex Backgrounds Cause Serious False Alarms. Moreover, The Ultra-tiny Objects Increase The Difficulty Of Accurate Detection. To Tackle These Problems, We Propose A Unified And Self-reinforced Network Called Remote Sensing Region-based Convolutional Neural Network (R2-CNN), Composing Of Backbone Tiny-Net, Intermediate Global Attention Block, And Final Classifier And Detector. Tiny-Net Is A Lightweight Residual Structure, Which Enables Fast And Powerful Features Extraction From Inputs. Global Attention Block Is Built Upon Tiny-Net To Inhibit False Positives. Classifier Is Then Used To Predict The Existence Of Target In Each Patch, And Detector Is Followed To Locate Them Accurately If Available. The Classifier And Detector Are Mutually Reinforced With End-to-end Training, Which Further Speed Up The Process And Avoid False Alarms. Effectiveness Of R2-CNN Is Validated On Hundreds Of GF-1 Images And GF-2 Images That Are 18 000×18 192 Pixels, 2.0-m Resolution, And 27 620 × 29 200 Pixels, 0.8-m Resolution, Respectively. Specifically, We Can Process A GF-1 Image In 29.4 S On Titian X Just With Single Thread. According To Our Knowledge, No Previous Solution Can Detect The Tiny Object On Such Huge Remote Sensing Images Gracefully. We Believe That It Is A Significant Step Toward Practical Real-time Remote Sensing Systems.
Security Of Internet Is Becoming The Latest Important Concerns Along With Extensive Application Of The Internet. All Elements That Compose Security System Over Maliciously Action Can Display The Performance Though Accomplish Harmony Of Security Elements Perfectly. Weakness Of Some Part Causes Fatal Result To Whole Security System. Therefore, Security Systems Need Elaborate Design And Mutual Coordination In Each Element. In This Paper, We Propose The Method Of One Time Password Key Generation Of OTP Using Fingerprint Features. Fingerprint Is Powerful Personal Authentication Factors, And It Can Create Variable Password Key For One Time Using Information Of Fingerprint Features. And We Will Perform A Simulation For Proposed Password Key Generation Method.