Video Manipulation Detection via Recurrent Residual Feature Learning Networks Over the past decade, the increased adoption of Internetconnected technology has led to a substantial growth in the amount of videos produced and digested by people around the world. Subsequently, manipulated content within videos has become far more common and difficult to detect. Material of this nature poses a significant problem as it provides a way to falsely affect viewersxe2x80x99 beliefs. In this work, we first develop a pipeline to generate manipulated video segments from preexisting videos and then develop a deep learning architecture to detect unique video manipulations on a framebyframe basis. Specifically, we develop a model which analyzes manipulated video segments represented as sequences of images via a joint Residual Network (ResNet) feature extractor and Long ShortTerm Memory (LSTM) network to detect video frames that exhibit signs of manipulation and classify which type of manipulation was applied. We train our model on a modified subset of the UCF101 Action Recognition Dataset 1 which we alter with a set of four manipulation types: object insertion, compression, frame blackout, and blurring. We evaluate the classification accuracy of our model by creating manipulation blocks, or sets of consecutive manipulated video frames, varying block length, distance between blocks, and manipulation distribution within blocks. Experimental results demonstrate that our model achieves high (u003e90%) classification accuracy in the presence of increased frame class transitions.

ResFlow: Multitasking of Sequentially Pooling Spatiotemporal Features for Action Recognition and Optical Flow Estimation Since deeplearningbased method has been widelyused and is capable of generating generic model, most existing methods about action recognition use either twostream structure, considering spatial and temporal features separately, or C3D, costing lots of prices in memory and time. We aim to design a robust system to extract spatiotemporal features with aggregation mechanism to integrate local features in temporal order. In light of this, we propose ResFlow to estimate optical flow and predict action recognition simultaneously. Leveraging the characteristic of optical flow estimation, we extract spatiotemporal feature via an autoencoder. Via a novel Sequentially Pooling Mechanism which literally pool global spatiotemporal feature sequentially, we extract spatiotemporal feature at each time and aggregate these local features into global feature. This design use only RGB images as input with temporal information encoded, pretrained by optical flow, and sequentially aggregate spatiotemporal features in high efficiency. We evaluate our ability of estimating optical flow on FlyingChairs dataset and show the promising results of action recognition on UCF101 dataset through a series of experiments.