## 2 Literature Survey  
Concealing information in other media is extremely old, as depicted on account of steganography. The term advanced watermarking first appeared in 1993 when [PERSON] et al. [12] introduced two methods to conceal information in pictures. In the recent past, telehealth systems have increasingly improved watermarking approaches [13]. These approaches provide authentication and security and give optimal bandwidth utilization, another essential criterion of the telehealth communication system. In [13], the authors proposed a watermarking scheme (WS) for telehealth applications. This method embedded a signature watermark image and patient report of 80 characters in length using lifting wavelet transform (LWT) and discrete cosine transform (DCT) schemes. This work decomposed the host image into subbands using LWT, and DCT further transformed the significant subbands. Simultaneously, the patient report and the signature watermark were encrypted and embedded into the final DCT-transformed subbands. The reverse process was applied to extract the watermarked information. A blend of watermarking, cryptography, and error-correcting code for electronic patient records (EPRs) is proposed in the method [14]. The watermarking image (WI) and EPR are embedded in this method after performing DWT and turbo encoding, respectively. Later, an inverse DWT is applied to this embedded data and processed to obtain an encrypted watermark using pallier encryption. Simultaneously, the cover image is encrypted using a pallier cryptosystem to obtain an encrypted cover image. Finally, these encrypted images (cover and watermark) are again embedded and sent via a communication channel. The reverse process is held at the destination to obtain the desired information. A crypto watermarking scheme for telemedical applications is proposed in [15]. In [16], a blind WS is framed to hide EPR data in the retinal image for telehealth applications. The retinal image is decomposed into subbands, and the lower subband (LL) is subjected to SVD; the EPR watermark is placed into this band. At the initial step, a bit-plane extraction is performed on the host image, and the watermark image is integrated into the host image using a chaotic mapping scheme. Further, the watermarked image (WI) is encrypted using the fractional Hartley transform to obtain the crypto-watermarked image. Another work in [17] is proposed for the detection of tampered medical images using a crypto WS. However, it is a non-blind watermarking scheme, where the EPR is embedded into the radiological images for authentication and security. The approach is based on DCT and compressive sensing (CS), where CS is used to encrypt the watermark data and DCT is applied to a host image. In [18], the authors attempted to embed patients' two biometrics, fingerprints and face, using a two-stage watermarking approach. Initially, the fingerprint was encrypted using minutiate extraction and encoding into the original face image and a key. Later, the watermarked image was further encrypted and embedded into the original fingerprint to obtain the second level of the WI. Finally, the watermark was embedded by combining the DCT subbands and the encrypted watermark image. All the literature above is related to image watermarking with different transform domains. They follow different types of watermarking approaches based on extraction, and in embedding the watermark, different transformation techniques methods are utilized (most commonly, DWT, DCT, and LWT). Previous watermarking schemes are weak in terms of the security of watermark data, which inspired us to add crypto techniques to protect the watermark. The proposed watermarking approach can overcome authentication problems by embedding the QR code watermark. The proposed scheme converts the text information to a scrambled QR image using a chaotic logistic map. The public and private keys are used to cipher and decipher the data.