{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":113558,"databundleVersionId":14174843,"sourceType":"competition"}],"dockerImageVersionId":31153,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"### A dummy submission solution that predicts all test samples as “authentic”","metadata":{}},{"cell_type":"code","source":"import os\nimport pandas as pd\nfrom glob import glob\n\nDATA_PATH = DATA_PATH=\"/kaggle/input/recodai-luc-scientific-image-forgery-detection/\" \n\n# ==================== CREATE DUMMY SUBMISSION ====================\ntest_image_paths = sorted(glob(os.path.join(DATA_PATH, \"test_images\", \"*.png\")))\n\nsubmission_data = [\n    {\"case_id\": os.path.splitext(os.path.basename(p))[0], \"annotation\": \"authentic\"}\n    for p in test_image_paths\n]\n\nsubmission_df = pd.DataFrame(submission_data)\nsubmission_df.to_csv(\"submission.csv\", index=False)\n\nprint(\"Submission created!\")\nprint(f\"Total test images: {len(submission_df)}\")\nprint(submission_df.head())\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-10-31T09:07:36.560928Z","iopub.execute_input":"2025-10-31T09:07:36.561281Z","iopub.status.idle":"2025-10-31T09:07:36.572610Z","shell.execute_reply.started":"2025-10-31T09:07:36.561258Z","shell.execute_reply":"2025-10-31T09:07:36.571566Z"}},"outputs":[],"execution_count":null}]}