Like traditional physical watermarks, digital watermarks are often only perceptible under certain conditions, e.g. after using some algorithm. If a digital watermark distorts the carrier signal in a way that it becomes easily perceivable, it may be considered less effective depending on its purpose. Traditional watermarks may be applied to visible media (like images or video), whereas in digital watermarking, the signal may be audio, pictures, video, texts or 3D models. A signal may carry several different watermarks at the same time. Unlike metadata that is added to the carrier signal, a digital watermark does not change the size of the carrier signal.
Both steganography and digital watermarking employ steganographic techniques to embed data covertly in noisy signals. While steganography aims for imperceptibility to human senses, digital watermarking tries to control the robustness as top priority.
One application of digital watermarking is source tracking. A watermark is embedded into a digital signal at each point of distribution. If a copy of the work is found later, then the watermark may be retrieved from the copy and the source of the distribution is known. This technique reportedly has been used to detect the source of illegally copied movies.
The information to be embedded in a signal is called a digital watermark, although in some contexts the phrase digital watermark means the difference between the watermarked signal and the cover signal. The signal where the watermark is to be embedded is called the host signal. A watermarking system is usually divided into three distinct steps, embedding, attack, and detection. In embedding, an algorithm accepts the host and the data to be embedded, and produces a watermarked signal.
Detection (often called extraction) is an algorithm which is applied to the attacked signal to attempt to extract the watermark from it. If the signal was unmodified during transmission, then the watermark still is present and it may be extracted. In robust digital watermarking applications, the extraction algorithm should be able to produce the watermark correctly, even if the modifications were strong. In fragile digital watermarking, the extraction algorithm should fail if any change is made to the signal.
A digital watermarking method is referred to as spread-spectrum if the marked signal is obtained by an additive modification. Spread-spectrum watermarks are known to be modestly robust, but also to have a low information capacity due to host interference.
A digital watermarking method is said to be of quantization type if the marked signal is obtained by quantization. Quantization watermarks suffer from low robustness, but have a high information capacity due to rejection of host interference.
A digital watermarking method is referred to as amplitude modulation if the marked signal is embedded by additive modification which is similar to spread spectrum method, but is particularly embedded in the spatial domain.
The evaluation of digital watermarking schemes may provide detailed information for a watermark designer or for end-users, therefore, different evaluation strategies exist. Often used by a watermark designer is the evaluation of single properties to show, for example, an improvement. Mostly, end-users are not interested in detailed information. They want to know if a given digital watermarking algorithm may be used for their application scenario, and if so, which parameter sets seems to be the best.
Epson and Kodak have produced cameras with security features such as the Epson PhotoPC 3000Z and the Kodak DC-290. Both cameras added irremovable features to the pictures which distorted the original image, making them unacceptable for some applications such as forensic evidence in court. According to Blythe and Fridrich, \"[n]either camera can provide an undisputable proof of the image origin or its author\".A secure digital camera (SDC) was proposed by Saraju Mohanty, et al. in 2003 and published in January 2004. This was not the first time this was proposed. Blythe and Fridrich also have worked on SDC in 2004  for a digital camera that would use lossless watermarking to embed a biometric identifier together with a cryptographic hash.
The digital watermarking images have various forms of attacks, including loss compression, additive noise, geometric deformation, and background subtraction attacks, which may potentially influence watermarked digital images [31, 32]. Salt-and-pepper noise and multiplicative Gaussian noise attacks are the most popular noises that threaten digital watermarking images. For a simple 8-bit grayscale image, salt-and-pepper noises modify the value of each pixel into 0 or 255 (white and white), while cumulative Gaussian noises lower the quality of an image's visual appearance. Image-filtering attacks using averaging, Wiener, median, and Gaussian filtration can effectively erase a watermark that was embedded in a digital image. The median filter is defined as a nonlinear adaptive filtering approach that maintains an image's boundaries while eliminating noise. The Wiener filter is widely used to remove blur from images. The averaging filter lowers brightness variance among pixels by substituting every pixel's value well with the weighted mean of its own neighbors and itself. The Gaussian filter is commonly adopted to blur images while reducing intensity and distortion. Geometric attacks can cause image distortions generated through processes including scaling, rotating, clipping, and translating [33, 34]. Geometric attacks are divided into two categories: local and global. Local geometric threats, namely, chopping attacks, influence parts of the image, whereas global geometric attacks, like rotational and scale attacks, disrupt every pixel in an image. To enhance the durability of different types of geometric attacks, numerous methods have been proposed. 59ce067264