{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:36Z","timestamp":1758672936719,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>CAM-based methods are widely-used post-hoc interpretability methods that produce a saliency map to explain the decision of an image classification model. The saliency map highlights the important areas of the image relevant to the prediction. In this paper, we show that most of these methods can incorrectly attribute an important score to parts of the image that the model cannot see. We show that this phenomenon occurs both theoretically and experimentally. On the theory side, we analyze the behavior of GradCAM on a simple masked CNN model at initialization. Experimentally, we train a VGG-like model constrained to not use the lower part of the image and  nevertheless observe positive scores in the unseen part of the image. This behavior is evaluated quantitatively on two new datasets. We believe that this is problematic, potentially leading to mis-interpretation of the model's behavior.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/1220","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"10937-10941","source":"Crossref","is-referenced-by-count":0,"title":["CAM-Based Methods Can See through Walls (Extended Abstract)"],"prefix":"10.24963","author":[{"given":"Magamed","family":"Taimeskhanov","sequence":"first","affiliation":[{"name":"Julius-Maximilians-Universit\u00e4t W\u00fcrzburg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronan","family":"Sicre","sequence":"additional","affiliation":[{"name":"Centrale M\u00e9diterran\u00e9e"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Damien","family":"Garreau","sequence":"additional","affiliation":[{"name":"Julius-Maximilians-Universit\u00e4t W\u00fcrzburg"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:36:29Z","timestamp":1758627389000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/1220"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/1220","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}