Geometrically invariant digital watermarking schemes based on robust feature detectors are proposed in this thesis. First, three types of feature detectors are proposed for digital image watermarking: the Edge Based Feature Detector, the SIFT Based Feature Detector, and the Adaptive Harris Based Detector. The Edge Based Feature Detector is proposed based on edge detection and it can extract a unique feature in the specific region. The SIFT Based Feature Detector is proposed by improving SIFT algorithm to produce more robust feature points for digital image watermarking, and it can extract number of feature points. The Adaptive Harris Based Detector is proposed by revamping and enhancing the Harris corner detector and it can also extract a number of reliable feature points. The three detectors are proven to be highly robust against both geometric attacks and also common signal processing. After locating the features for watermarking, two watermarking methods for different types of watermark are proposed: the histogram distribution based watermarking method, for a sequence of watermark data bits. And the Zernike transform based watermarking method for embedding data sequence of specific distribution and detecting its existence during the watermark extraction process. Besides digital images, the feature extraction based watermarking scheme can also be applied in digital audio clips as well. The Robust Audio Feature Detector is proposed to extract features from digital audio clips. Then, the Stationary Wavelet Transform is applied to the extracted regions, and thus the regions are decomposed into approximation and detail coefficients. Afterwards, the watermark is embedded / iv extracted into / from the approximation coefficients with the spread spectrum communication techniques. Experiments are conducted to evaluate the performance of the proposed watermarking schemes. The proposed algorithms are proven to be robust against most of the attacks, including common signal /audio processing and geometric distortions. Furthermore, they outperform the existing representative works when under common signal / audio processing and geometric distortions.