Image Encryption For Secure Internet Transfer

A tool that encrypts images with RSA before sending them over the internet. The image is converted to hex, encrypted and transferred to the receiver's software, which decrypts it.

A camera or uploaded image provides the input frame. OpenCV pre-processes it: grayscale conversion, noise removal and resizing. A detector or recognition model finds and identifies the region of interest. The result is displayed, logged in the database and can trigger an alert or action.

Capture the frame, convert to grayscale, remove noise with a Gaussian blur, segment the region of interest with thresholding or edge detection, extract features and finally classify or match them.

First a detector (Haar cascade, HOG or a CNN) finds the face. Then a model converts it into an embedding, a list of numbers that describes the face, and compares it with stored embeddings using a distance measure.

Symmetric encryption (AES, DES) uses one shared key for encryption and decryption and is fast. Asymmetric encryption (RSA) uses a public key to encrypt and a private key to decrypt, which solves key sharing but is slower.

A hash is one-way, so even the administrator cannot recover the password. We add a random salt and use a slow hash such as bcrypt so stolen hashes are hard to crack.

It follows a three-tier architecture: the browser shows the user interface, the server-side code handles logic and validation, and the MySQL database stores records. Requests travel over HTTP and responses are rendered as HTML or JSON.

We followed an iterative (incremental) model: requirements and a basic design first, then modules built and tested one by one, with the guide's feedback after each review. It suits a project where requirements become clearer as we build.

Unit testing of each module, integration testing of the complete flow and system testing with real inputs. Test cases with expected and actual results are listed in the report, including invalid inputs.

The current version is tested on a limited dataset or number of users. Future work includes deploying on the cloud, adding more features or languages, improving accuracy with more data and building a mobile version.