ALimitations777 As described in Sections 4 and 6, users would tailor attacks to image clusters. In the case of beige778 box, we outright provided these clusters by disclosing which image indices corresponded to which779 general watermark type. For the black-box track, several winning teams clustered images into groups780 by artifact varieties and did so by hand. For the latter, this was made possible because (1) our data set781 was relatively small, enabling this type of manual data labeling, and (2) they were made aware that782 the dataset contained mixtures of several watermarks. A database owner who uses only one type of783 watermark will unlikely produce such variation in artifacts.784 Additionally, we use the watermark models and setting provided in the original papers and do not785 calibrate the strength of watermarks.

Understanding, reasoning, and manipulating semantic concepts of images have been a fundamental research problem for decades. Previous work mainly focused on direct manipulation of natural image manifold through color strokes, key-points, textures, and holes-to-fill. In this work, we present a novel hierarchical framework for semantic image manipulation. Key to our hierarchical framework is that we employ structured semantic layout as our intermediate representations for manipulation. Initialized with coarse-level bounding boxes, our layout generator first creates pixel-wise semantic layout capturing the object shape, object-object interactions, and object-scene relations. Then our image generator fills in the pixel-level textures guided by the semantic layout. Such framework allows a user to manipulate images at object-level by adding, removing, and moving one bounding box at a time. Experimental evaluations demonstrate the advantages of the hierarchical manipulation framework over existing image generation and context hole-filing models, both qualitatively and quantitatively. Benefits of the hierarchical framework are further demonstrated in applications such as semantic object manipulation, interactive image editing, and data-driven image manipulation.

Extensive experiments show that our method generates engaging manipulation results conforming to the transformations entailed in demonstrations.

This process enables the value (feature maps) to be rearranged (through a weighted sum) to align with the form of the query, thereby reflecting their strong correspondence. The input noise is removed because its stochasticity slows down the training. Given the need for balancing between high correspondence and image quality, we empirically set the weights of our loss terms. To demonstrate the influence of the additional losses introduced in our method, we provide both quantitative and qualitative ablations in Figure S2 and S3, respectively. Nonetheless, caution is warranted when overly increasing the number of clusters.

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Neural Information Processing Systems http://nips.cc/