Active Image Indexing
Not on the map. Why?
Abstract (arXiv)
Image copy detection and retrieval from large databases leverage two components. First, a neural network maps an image to a vector representation, that is relatively robust to various transformations of the image. Second, an efficient but approximate similarity search algorithm trades scalability (size and speed) against quality of the search, thereby introducing a source of error. This paper improves the robustness of image copy detection with active indexing, that optimizes the interplay of these two components. We reduce the quantization loss of a given image representation by making imperceptible changes to the image before its release. The loss is back-propagated through the deep neural network back to the image, under perceptual constraints. These modifications make the image more retrievable. Our experiments show that the retrieval and copy detection of activated images is significantly improved. For instance, activation improves by +40% the Recall1@1 on various image transformations, and for several popular indexing structures based on product quantization and locality sensitivity hashing.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 34 of its 66 references that are in the map. First posted on arXiv 5 Oct 2022.
Builds on
34 of 66 references.
References · 34
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22 more
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Built on by
Citing papers per year: 2023: 2; 2024: 4; 2025: 2.
8 papers.
- 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Modelscited by 289
- 2024Watermark Anything with Localized Messagescited by 49
- 2024WMAdapter: Adding WaterMark Control to Latent Diffusion Modelscited by 29
- 2024AnyPattern: Towards In-context Image Copy Detectioncited by 5
- 20233rd Place Solution to Meta AI Video Similarity Challengecited by 2
- 2024Relational Self-supervised Distillation with Compact Descriptors for Image Copy Detectioncited by 1
- 2025Origin Identification for Text-Guided Image-to-Image Diffusion Modelscited by 1
- 2025Task-Agnostic Attacks Against Vision Foundation Modelscited by 1
- 2025Task-Agnostic Attacks Against Vision Foundation Models5 Mar 2025
- 2025Origin Identification for Text-Guided Image-to-Image Diffusion Models4 Jan 2025
- 2024Watermark Anything with Localized Messages11 Nov 2024
- 2024WMAdapter: Adding WaterMark Control to Latent Diffusion Models12 Jun 2024
- 2024Relational Self-supervised Distillation with Compact Descriptors for Image Copy Detection28 May 2024
- 2024AnyPattern: Towards In-context Image Copy Detection21 Apr 2024
- 20233rd Place Solution to Meta AI Video Similarity Challenge24 Apr 2023
- 2023The Stable Signature: Rooting Watermarks in Latent Diffusion Models27 Mar 2023