To address the issue of decreased reliability in power grid fault detection and state estimation based on synchronized measurement data, caused by network attack threats such as false data injection resulting from the exposure of vulnerable nodes in power systems, a model-free synchronized phasor data source authentication method based on one-dimensional time series visualization is proposed. Firstly, to improve the authentication speed, the piecewise aggregate approximation algorithm is adopted to reduce the dimensionality of the original grid frequency time series, lowering computational complexity while retaining characteristic frequency information. Secondly, to preserve the absolute temporal relationship of the time series, the Gramian angular difference field method is employed to transform the one-dimensional grid frequency time series into a two-dimensional visualization. Then, to capture grid frequency features at different scales, dilated convolution is applied to obtain a large receptive field while ensuring authentication speed. Finally, validation experiments are conducted using measured multi-point synchronized phasor measurement data. Comparative analysis with different authentication methods verifies the effectiveness and feasibility of the proposed approach. Experimental results demonstrate that the model-free authentication method based on a visualized dilated convolutional neural network achieves a test accuracy of 94.76%, and can effectively authenticate synchronized phasor data sources at various locations.